Hi guys! Getting an error after hours of fooling with this trying to get it to work fixing some errors and gaining others. I will get straight to it, I had to remove Image scale to Side because I couldn't get the Node working no matter what so I found a new Scale to Side, hooked it up to all the same boxes hoping it would work. Well I am now getting more errors and cant figure out why I will post the Original code and my edit and the error, I would really appreciate any help!!!
Here is the Original json: https://drive.google.com/file/d/1fmq0MWgohazCQsw4b3g2aN5uOwVh-ywm/view?usp=sharing
Here is my Edited one with the new Scale to Side node, not sure if my edits caused it, I hooked everything up the same... really need a hand! - https://drive.google.com/file/d/1xSXD83XESpOF0-gTFSHUjYXKRalhNsv4/view?usp=sharing
and here is my error:
Error occurred when executing KSampler:
division by zero
File "/workspace/comfyui_launcher_projects/style_transfecomfyui/execution.py", line 151, in recursive_execute output_data, output_ui = get_output_data(obj, input_data_all) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/execution.py", line 81, in get_output_data return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/execution.py", line 74, in map_node_over_list results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/nodes.py", line 1344, in sample return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/nodes.py", line 1314, in common_ksampler samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/sample.py", line 37, in sample samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 755, in sample return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 657, in sample return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 644, in sample output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 623, in inner_sample samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 534, in sample samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) File "/workspace/comfyui_launcher_projects/style_transfevenv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/k_diffusion/sampling.py", line 707, in sample_dpmpp_sde_gpu return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) File "/workspace/comfyui_launcher_projects/style_transfevenv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/k_diffusion/sampling.py", line 539, in sample_dpmpp_sde denoised = model(x, sigmas[i] * s_in, **extra_args) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 272, in __call__ out = self.inner_model(x, sigma, model_options=model_options, seed=seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 610, in __call__ return self.predict_noise(*args, **kwargs) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 613, in predict_noise return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 258, in sampling_function out = calc_cond_batch(model, conds, x, timestep, model_options) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/samplers.py", line 192, in calc_cond_batch c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/controlnet.py", line 155, in get_control control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/controlnet.py", line 173, in get_control self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device) File "/workspace/comfyui_launcher_projects/style_transfecomfyui/comfy/utils.py", line 406, in common_upscale new_aspect = width / height

the julia code for a basically the same algorithm as native code performance and precision

function ffsqrtn(z::Float32,℧::Int32=Int32(5.32496253e8))::Float32 i = reinterpret(Float32,((reinterpret(Int32,z) >> 1) + ℧)) y = i - 0.5*(i - z/i) y = y - 0.5*(y - z/y) return y - 0.5*(y - z/y) end 

a really good explanation can be found here https://www.youtube.com/watch?v=p8u_k2LIZyo
in short we are doing 3 steps of the newtons method for precession purposes with large numbers
and the first line of code is basically a log2(1-x) approximation so in the interval [0,1] we have that log_2(1+x) is around x and happens that the float32 bit representation is basically the "same" as "x-1" so technically log(x) is around the same as the Float32 bit representation of x we can add a correction factor for making it closer in this case we take mu = 0.0430 and if we do the math log_2(a) = log_2(x^(1/2)) if and only if a = sqrt(x) so if we solve for the float32 bit representation of a we get F_a = 1/2*(F_x) + C with F_a and F_x being the 32bit float representation of a and x and C a constant equal to -2^22*mu + 2^22*127 = 5.32496253e8 "our magic number"
​
so we get the "number" corresponding to F_x we do a bit shift equivalent to dividing by two "(reinterpret(Int32,z) >> 1)" and then we add our magic number that would be equal to F_a so we turn it into a floating point number with reinterpret(Float32, 1/2F_x + C)
and we do the newtons method
too much chitchating lets see the stats
https://preview.redd.it/m6wqs84xf1pc1.png?width=1920&format=png&auto=webp&s=02186fc613de62ae7fe3504f31010b66a9ee583f
using BenchmarkTools
\@benchmark ffsqrtn(2.1474836f9)
min time … max time: 1.409 ns … 24.934 ns
\@benchmark sqrt(2.1474836f9)
min time … max time: 1.409 ns … 29.104 ns
​
EXACTLY THE SAME!!, even better 24.934 ns our algo and 29.104 ns the sqrt function!!!
enjoy!
​
​

Back in April of 2023, this post from twitter user @RhysLindmark made the rounds on magic twitter and on reddit, endeavoring to quantitively rank the top players on Arena. Nearly a year later, we’re due for an update, and I wanted to make a few changes to the methodology while we’re at it. (Or feel free to skip to the bottom if you don’t care how the sausage got made.)
 

1. Total trophies > total wins. Sorting by most total trophies will pull in more of the strong players winning at elite rates, taking the place of the folks who brute force their way onto the total wins leaderboard despite unremarkable winning percentages (a noble pursuit to be sure, but not relevant to a Best of the Best conversation).
2. Lump totals > Set by Set data. Rhys’s analysis pulled 15 sets’ leaderboard data one by one, but I can’t think of a compelling argument against simply pulling everything all at once. 17Lands allows you to remove the set filter from its leaderboard and pull the 500 users with the most total trophies in their full 17Lands career, across all formats.
3. Winning percentage > Total Wins. As long as we’re confident that the sample size is sufficiently large to smooth out variance, the top players are the ones who win at the highest rates, not the ones who play the most games.
4. Traditional Draft matters too! I pulled top-500s from both the Premier Draft and Traditional Draft leaderboards. (Sorry Quick Draft.)

 
Pulling the trophy leaderboards for both event types produces a list of 873 unique players, as 126 17Lands users appear on both leaderboards. (Astute arithmeticians will note that 873 + 126 adds up to 999 rather than 1000. This is because 17Lands user DullUserName appears on the Traditional Draft trophy leaderboard twice somehow. DullUserName indeed!)
 
These 126 duplicate drafters are of great use, as they allow us to dig into the age-old question of just how much easier is it to win in Traditional Draft than in Premier? First, we have to reverse-engineer everyone’s Trad Draft game win % from their Trad Draft match win %, since 17Lands only provides the latter. The formula for estimating Bo3 match win % from a given game win % is:

Match Win % = [Game Win %]^2 * (3 - 2 * [Game Win %]) 

To invert this, I graphed out the above formula, flipped the x and y axis, and fit a curve to the resulting line, producing a derived Game Win % formula of:

Game Win % = 0.6581 * [Match Win %]^3 - 0.9884 * [Match Win %]^2 + 1.1498 * [Match Win %] + .0898. 

 
Trad Draft Derived Game Win %
 
Convert our users’ Trad Draft Match Win % to Game Win %, and we now have 126 data points of users’ Trad Draft Game Win % vs their Premier Draft Game Win %. Below, I plotted the delta between the two winrates against users’ total Premier Drafts logged.
 
Trad Draft Game Win % vs Premier Draft Game Win %
 
We see clear evidence that high volume Arena limited players will find easier competition in Traditional Draft, as 94% of these users produced a higher winrate in Traditional Draft than in Premier. The weighted average of this differential is a +3.34%. But we get an even more precise accounting from the linear regression line applied above:

[Trad Draft WR] - [Premier Draft WR] = 2.2348% + (0.12% * [Total Premier Drafts]) 

 
This formula tells us that these users are winning +2.23% more of their Trad Draft games at a baseline, plus that gap widens by an additional 1.2% for each 1000 Premier Drafts drafts logged. So not only is this useful for comparing win rates in Trad Draft to Premier Draft on an even playing field, it’s an invaluable tool for win rate comparison within the Premier Draft trophy leaderboard. Users toward the bottom with ~300 total Premier Drafts logged can manage their rank to minimize time spent in Mythic, whereas the 1000+ Premier Draft grinders will unavoidably spend the majority of their time there.
 
Armed with this, we can now pull together all the data and make an apples-to-apples comparison between Trad and Premier, low-volume Premier and high-volume Premier. (Adjusting Premier Draft Game Win % upward to normalize it to the observed lower competition level found in Traditional Draft. In effect, calculating each users' game win % if all of their games were played in Traditional Draft.)

• Traditional Draft leaderboard derived game win % stays as-is. There’s no advantage or disadvantage to playing more or fewer total Trad Drafts; your win rate should stay the same whether you play 200 or 2000.
• Premier Draft leaderboard game win percentage is converted to Adjusted Premier Draft Win %, using the formula above: Adj Premier Draft Game Win % = Premier Draft Win % + 2.2348% + .12% * Total Premier Drafts.
• Users who appear on both leaderboards get a hybrid winning percentage, combining both metrics above and weighting for total drafts of each.

 
Without further ado, the top 50 Arena Drafters on 17Lands:

Rank	Screen Name	Total Premier Drafts	Premier Draft Game Win %	Adjusted Premier Draft Game Win %	Total Trad Drafts	Derived Trad Draft Game Win %	Overall Game Win %
1	Naemen	310	69.5	72.1			72.1
2	Eken	1670	67.4	71.6			71.6
3	JiRock	262	66.7	69.2	910	71.9	71.3
4	Bond2King	423	68.5	71.2			71.2
5	Voldraek				222	71.2	71.2
6	Daedylus	403	68.3	71			71
7	churro				138	70	70
8	Trumpetman	309	68.7	71.3	315	68.2	69.7
9	Matignon	380	67.3	70	147	68.4	69.5
10	luvemNleavum	1163	65.8	69.4	679	69.7	69.5
11	Residentevil0324	457	66.6	69.4			69.4
12	Icky	2041	65.1	69.8	304	66.7	69.4
13	gemaide	328	66.7	69.3			69.3
14	Solola	739	66.2	69.3			69.3
15	Sene				147	69.3	69.3
16	BeersSC	1512	65.2	69.2			69.2
17	Boz	658	66.7	69.7	235	67.6	69.2
18	Nummy	2223	64.1	69			69
19	littlebeep	702	65.7	68.8			68.8
20	Just Lola	1702	64.8	69.1	752	68.1	68.8
21	Tali	768	65.6	68.8			68.8
22	Beschi	360	66	68.7			68.7
23	Ekil	315	66.1	68.7	902	68.6	68.7
24	JasonYe4273	304	64.6	67.2	762	69	68.5
25	Dafore	1686	64.2	68.5			68.5
26	ElPalito	1043	65.1	68.6	548	68.2	68.4
27	DarkestMage	458	65.6	68.4			68.4
28	Bagzoo	253	65.8	68.3			68.3
29	Consecrated Sphinct	294	65.7	68.3	164	68.4	68.3
30	DraftPunk	760	65.3	68.4	485	67.9	68.2
31	JohnnyD	322	65.6	68.2			68.2
32	Worldwaker2	278	65.5	68.1			68.1
33	Neo	743	64.9	68			68
34	Stapler	571	65.1	68			68
35	Shirkka	315	65.4	68			68
36	SamuelHBlack	1426	64.1	68	200	67.8	68
37	Razgorth				1325	67.9	67.9
38	qtasky	617	64.9	67.9			67.9
39	shmee	492	65.5	68.3	303	67	67.8
40	CloakedByMist	398	65.1	67.8			67.8
41	greghatch	477	65	67.8			67.8
42	Narchon				243	67.8	67.8
43	Chord_O_Calls	2273	62.8	67.8			67.8
44	OysteinPrytz	1158	64.1	67.7			67.7
45	Ncaa	1958	63.6	68.2	336	64.8	67.7
46	twoduckcubed	2284	63	68	433	65.8	67.6
47	aigra	539	64.6	67.5	300	67.8	67.6
48	rst38				724	67.5	67.5
49	Pumbles Mumbles	482	64.7	67.5			67.5
50	Kankedort	278	69.5	72.1	831	65.9	67.5

 
Top 50 Arena Drafters on 17Lands
Full List of 873 (+LSV manually added)

I feel like im getting closer and closer to being able to run a faceswap on a video in comfyui. Im using this workflow I dont remember where I got it but Im sure it was a workflow database site. Anyone out there can help me to figure out what model file im supposed to use in it?

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Multiline","pos":[-3164.2721246847536,-224.33515932957212],"size":{"0":280,"1":130},"flags":{},"order":0,"mode":0,"outputs":[{"name":"STRING","type":"STRING","links":[470],"shape":3,"slot_index":0}],"title":"Append text","properties":{"Node name for S&R":"Text Multiline"},"widgets_values":["highly detailed, realistic, city streets, night sky"],"color":"#232","bgcolor":"#353"},{"id":280,"type":"Text Multiline","pos":[-3164.2721246847536,-434.33515932957243],"size":{"0":280,"1":160},"flags":{},"order":1,"mode":0,"outputs":[{"name":"STRING","type":"STRING","links":[468],"shape":3,"slot_index":0}],"title":"Prepend text","properties":{"Node name for S&R":"Text Multiline"},"widgets_values":["RAW photo of man, cyberpunk, neon glowing jacket, futuristic, robotic,"],"color":"#232","bgcolor":"#353"},{"id":7,"type":"CLIPTextEncode","pos":[-2854.2721246847536,-54.335159329572164],"size":{"0":425.27801513671875,"1":180.6060791015625},"flags":{},"order":19,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":213}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[384],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["nude, nsfw, canvas frame, cartoon, 3d, out-of-frame, ugly, cross-eyed, embedding:JuggernautNegative-neg"],"color":"#572e1a","bgcolor":"#6b422e"},{"id":271,"type":"VHS_VideoCombine","pos":[-742.311258715686,130.2723187428326],"size":{"0":800,"1":290},"flags":{},"order":52,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":582},{"name":"audio","type":"VHS_AUDIO","link":null},{"name":"batch_manager","type":"VHS_BatchManager","link":null}],"outputs":[{"name":"GIF","type":"GIF","links":null,"shape":3}],"properties":{"Node name for S&R":"VHS_VideoCombine","ttNbgOverride":{"color":"#322","bgcolor":"#533","groupcolor":"#A88"}},"widgets_values":{"frame_rate":12,"loop_count":0,"filename_prefix":"AnimateDiff/AD","format":"video/h264-mp4","pix_fmt":"yuv420p","crf":19,"save_metadata":true,"pingpong":false,"save_output":true,"videopreview":{"hidden":false,"paused":false,"params":{}}},"color":"#322","bgcolor":"#533"},{"id":286,"type":"PreviewImage","pos":[87.68874128431665,130.2723187428326],"size":{"0":1470,"1":1260},"flags":{},"order":51,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":581}],"title":"Individual Frames","properties":{"Node name for 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S&R":"EditBasicPipe","ttNbgOverride":{"color":"#322","bgcolor":"#533","groupcolor":"#A88"}},"color":"#322","bgcolor":"#533"},{"id":177,"type":"Reroute","pos":[-1730.427841870304,-144.49830987512792],"size":[75,26],"flags":{},"order":38,"mode":0,"inputs":[{"name":"","type":"*","link":462,"pos":[37.5,0]}],"outputs":[{"name":"","type":"CONDITIONING","links":[516],"slot_index":0}],"properties":{"showOutputText":false,"horizontal":true},"color":"#322","bgcolor":"#533"},{"id":184,"type":"Reroute","pos":[-1010,70],"size":[75,26],"flags":{},"order":42,"mode":0,"inputs":[{"name":"","type":"*","link":517}],"outputs":[{"name":"","type":"CONDITIONING","links":[464],"slot_index":0}],"properties":{"showOutputText":false,"horizontal":false},"color":"#232","bgcolor":"#353"},{"id":176,"type":"Reroute","pos":[-1630.4278418703043,-144.49830987512792],"size":[75,26],"flags":{},"order":37,"mode":0,"inputs":[{"name":"","type":"*","link":542,"pos":[37.5,0]}],"outputs":[{"name":"","type":"CONDITIONING","links":[515],"slot_index":0}],"properties":{"showOutputText":false,"horizontal":true},"color":"#232","bgcolor":"#353"},{"id":307,"type":"ControlNetLoaderAdvanced","pos":[-2010.4278418703043,-34.498309875127816],"size":{"0":367.79998779296875,"1":58},"flags":{"collapsed":true},"order":2,"mode":0,"inputs":[{"name":"timestep_keyframe","type":"TIMESTEP_KEYFRAME","link":null,"slot_index":0}],"outputs":[{"name":"CONTROL_NET","type":"CONTROL_NET","links":[502],"shape":3,"slot_index":0}],"properties":{"Node 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​

Dogmatism, Dismissiveness, and Cognitive Inflexibility: The Inflation Between Subjective Feelings of Expertise and Tested Objective Expertise and How This Inflation Leads to Seriously Wrong Decisions From Alleged Experts
Crossposting audience: This is a new subreddit at zeronarcissists, the first anti-narcissism subreddit based on scientific evidence as far as I can tell. Please give us a follow at the original sub! We are new and growing
https://scholarship.miami.edu/esploro/outputs/doctoral/Does-Being-an-Expert-Make-You/991031447656202976
Does being an expert make you more negative?
Word of mouth is more influential because it shows increased relevancy to the consumer.

1. A survey conducted by Alloy Media & Marketing and Harris Interactive demonstrated that WOM is more credible and influential compared with other marketing and communication tactics such as TV commercials, magazine ads, and sampling (Creamer, 2005), because “it is perceived as having passed through the unbiased filter of ‘people like me’” (Allsop, Bassett, & Hoskins, 2007, p. 398).

Word of mouth allegedly generates more than twice the sales of paid advertising, which may be driving elicit ways to study and potentially even invade social networks.

1. Bughin, Doogan, and Vetvik (2010) suggest that WOM could generate “more than twice the sales of paid advertising” (p. 8). Overall, today WOM is incredibly important for both consumers and professionals, and for this reason deserves more attention from academia.

Differences between expert and nonexpert reviewers online using Word of Mouth online (such as Yelp reviews) were studied.

1. which types of consumers under what conditions would transmit or generate more eWOM (e.g., Chu & Kim, 2011; Liu, Zhao, & Qi, 2016), and so on. This study attempts to advance the understanding of eWOM communicators by exploring the differences in behavior patterns between expert and nonexpert reviewers.

Nonexperts are likely to use intuitions, emotions, and stereotypes as well as how they generally feel to inform a decision, while experts make decisions with rigor.

1. For example, research indicates that nonexperts are more likely to use peripheral cues and stereotypes to form attitudes or make decisions, whereas experts can process information more deeply and comprehensively (Mueller, Francis, & Lockshin, 2008; Rao & Monroe, 1998).

However, feeling like one is an “expert” can close the mind of an individual and show overclaim.

1. Studies have also found that expertise (subjective) can lead to close-mindedness, overconfidence, tendency of overclaim, and giving biased evaluations (Atir, Rosaenzweig, & Dunning, 2015; Fisher & Keil, 2016; Ottati, Price, Wilson, & Sumaktoyo, 2015).

Word of mouth online from expert sources was found more helpful and persuasive than non-expert sources.

1. . It was found that eWOM messages from expert sources were perceived as more helpful and hence more persuasive than information from non-expert sources (e.g., Bansal & Voyer, 2000; Cheung, Lee, & Rabjohn, 2008

Subjective expertise, and how one things of oneself as an expert or not, had a huge effect. It is not the same as objective expertise, which means scoring highly on measures of a skill.

1. To fill this gap in the burgeoning eWOM literature, this study focuses on the communicator’s self-perceived expertise, i.e., the communicator’s self-assessment of his/her own knowledge and expertise in a domain, which is also called “subjective expertise” (Liu, 2013; Mueller, Francis, & Lockshin, 2008)

Negative word of mouth is seen as more credible, more helpful and more important showing an irrational instinctual negativity bias when initially generating expertise. It relates to previous literature that warmth is seen as not competent, when in fact warm individuals scored higher than cold individuals on working memory tasks. It also shows that agreeableness sees the same fate as warmth as agreeable raters tend to rate things more highly and are seen as “not discriminatory enough”, yet the same is not applied to disagreeable people who are “unreasonably or capriciously discriminatory” as their equal in unhelpfulness, showing again irrational and old circuitry at play.

1. Message receivers tend to believe that negative WOM messages are more credible, more helpful, and more important, and thus are more likely to be persuaded by negative WOM messages. For example, Baek, Ahn, and Choi (2012) demonstrated that if a message contains more negative words, it is perceived as more helpful.

One star ratings have more effect than five star ratings, and show the person trying to establish power by giving them out. However, if the individual has excessively low expertise it will result in no power at all.

1. one-star ratings have a stronger influence on sales than five-star ratings, which suggests a negativity bias (Mizerski, 1982)

Expertise therefore is defined as the assessment of the knowledge, skill and experience required to provide accurate information.

1. negative eWOM has greater impact on company financing performances than positive eWOM. In addition to negative WOM messages, receivers also give more weight to information from sources with higher expertise. Expertise (also termed as competence, qualifications, authoritativeness), refers to the assessment of whether the communicator has the knowledge, skill, or experience required to provide accurate and valid information (Hilligoss & Rieh, 2008; O’Keefe, 2002)

Experts are deemed more accurate and credible and have stronger influence…until they begin to lose accuracy and credibility, if they do.

1. Expertise was deemed more accurate, credible, and as having stronger influence on receivers’ attitudes and behavioral intentions.

Knowledge and expertise were seen as the same.

1. ), whereas others use the terms expertise and knowledge interchangeably (e.g., Atir et al., 2015; Hadar et al., 2013). Following Hadar et al.’s (2003) suggestion, in this study there is no differentiation made between expertise and knowledge, and the two are considered notions of the same construct.

Objective expertise is how much actual knowledge one has stored in one’s memory, whereas subjective expertise is how knowledgeable one perceives oneself to be. Individuals may have the information stored more than an expert yet still feel they don’t know it and therefore fail to demonstrate expertise.

1. . Objective expertise concerns how much actual knowledge is stored in one’s memory (Carlson, Vincent, Hardesty, & Bearden, 2009; Hadar et al., 2013; Moorman, Diehl, Brinberg, & Kidwell 2004), whereas subjective and perceived communicator expertise are perceptions or individual judgments of one’s knowledge and expertise. Specifically, subjective expertise refers to the perception of one’s own expertise and knowledge in a given domain, or “the metacognitive feeling of knowing” (Hadar et al., 2013, p. 304; see also Carlson et al., 2009; Moorman et al., 2004).

People tend to overestimate what they actually know (Duning-Kruger)

1. d that people tend to overestimate what they actually know (Alba & Hutchinson, 2000; Duning, 2011; Kruger & Duning, 1999)

Source trustworthiness and expertise both factored in. An expert source that wasn’t trustworthy was not attuned to as much.

1. eWOM research, perceived source expertise, together with perceived source trustworthiness

Training, experience, and occupation influence perceptions of source expertise

1. factors that can affect perceived source expertise. Though not being empirically tested, factors such as the communicators’ training, experience, and occupation are believed to influence perceptions of source expertise, and have been manipulated by researchers in previous experiments (O’ Keefe, 2002)

“General misogyny”; the general population will find a male communicator more expert than a woman, even if that’s not correct objectively.

1. . For example, studies have found that in general, male communicators are deemed more expert than female communicators (Pearson, 1982).

Delivery, speaking rate, using citations, sidedness of arguments have been found to affect perceived source credibility.

1. In addition to source-related factors, message-related factors such as fluencies in delivery, speaking rate, using citations, sidedness of arguments (one-sided message vs. two-sided message) have been found to affect perceived source credibility as well (O’ Keefe, 2002).

Demographics, physical attractiveness, occupation, and position all contribute to feelings of expertise.

1. Regarding persuasiveness, in general messages from an expert source are more persuasive and can elicit greater attitude changes than those from a novice source. Adopting the framework of the Elaboration Likelihood Model (ELM), some of the aforementioned factors (such as demographic variables, physical attractiveness, occupation, and position)

When engaging with less rigorous thinkers, expertise will be analyzed on simple cues that do not actually show expertise, such as dress and gender.

1. low motivation or limited resources (i.e., time and ability) to process the information, they engage less in thinking and elaboration on the content/argument and instead make their judgments based on simple cues (Petty & Cacioppo, 1986). For example, factors such as physical attractiveness, position, occupation, race, and gender each require fewer cognitive resources

When given enough time, a message receiver undergoes a central route to scrutinize the message which leads to conformity with objective findings.

1. For example, when a message receiver has sufficient motivation, time, and ability to process a message, he/she undergoes a central route to scrutinize the message.
2. to make judgments (about source expertise as well as ultimate decisions) based on peripheral cues but rather on the actual quality of the argument reflected by various message features. Therefore, messages with certain features (such as a two-sided argument, strong argument)

More experience with a subject leads to over-enhancements of subjective expertise, without following increases in objective expertise (expertise “inflation”)

1. Objective expertise could be a stronger predictor than subjective expertise for various outcomes (Cordell, 1997), subjective expertise merits attentions because it is more malleable than objective expertise (Campbell, 2015). For example, research has demonstrated that more experience with the product category enhances both objective and subjective expertise, but this effect is stronger for subjective expertise than objective expertise (Park, Mothersbaugh, & Feick, 1994).
2. ), researchers could boost (or undermine) participants’ subjective expertise (Atir et al., 2015;

Giving someone a task before they are ready can be a way to try to make someone lose faith in their abilities, or misuse Duning-Kruger and the scientific method putting a hypothesis before facts (failed science) by giving them harder and harder content until they fail just to force their hypothesis against the way science works. This is not good science and not the meaning of Duning-Kruger.

1. . Those who completed a more (less) difficult task tended to perceive themselves as having lower (higher) expertise.

Experts tend to process information in a deeper more detailed way, and show signs of processing in a deeper and more detailed way. On the other hand, non-experts rely on single peripheral cues, sometimes to the complete failure to include actual content (bias).

1. A higher level of knowledge and expertise (determined using self-reporting measures) tend to process information in a deeper and more detailed way, making use of more attributes and considering the relationships between attributes (Mueller, Francis, & Lockshin, 2008; Rao & Monroe, 1988). Non-experts (self-perceived), on the other hand, usually rely on a single peripheral cue (e.g., price, country of origin) or several attributes independently in decision-making. Some researchers have found no significant difference between people with high subjective knowledge and low subjective knowledge (Guidry, Babin, Graziano, & Schneider, 2009).

Thinking one was an expert could promote being closed-minded and bias. A position can give someone an inflated and incorrect sense of competency, leading to many incompetent decisions.

1. high subjective expertise could promote close-mindedness and biased evaluation

When people believe they “know enough” about something, they stop searching for information which is required for precision no matter one’s level, and rather rely on feelings of knowing things. Ironically, in the experiment, this led to self-perceived experts saying they for sure “knew” about terms that didn’t even exist. And they said they knew about them certainly, due to the “expertise” in their field.

1. In other words, when people believe that they have sufficient knowledge, they stop processing or searching for information (necessary to achieve genuine knowledge or accurate judgment) and instead rely on the feeling of knowing and preexisting position to make a final (but likely biased) decision.
2. increased perceptions of one’s knowledge (subjective expertise) is linked to less information searching

Self-perceived expertise increases close-minded cognition

1. that self-perceived expertise indeed increases close-minded (dogmatic) cognition

Those who claimed to have more knowledge on a subject had greater bias than those who didn’t feel they had a lot of knowledge on it, and may be more likely to detect incongruencies due to the fresh eyes, which the “expert”, just like the false term experiment, will say don’t exist due to a mere feeling it doesn’t exist.

1. found that those who reported having more knowledge on a specific topic tended to possess greater bias than those reporting less self-perceived knowledge.

Increased subjective expertise lead to investment decisions, which explains instinctual common misogyny’s skewing influence on venture capital despite the facts

1. . They found that enhanced subjective expertise led to an increase in willingness to invest in a fund or investment program (Hadar et al., 2013).

Low subjective knowledge looked for more negative information, and gave less extreme evaluations, showing people who “need to hear good things” likely think they know more.

1. On the contrary, low subjective knowledge individuals sought out more negative information, and gave less extreme evaluations.

Satisfied experts like to generate more word of mouth messages than dissatisfied experts or non-experts

1. Results of data analysis show that satisfied experts like to generate more WOM messages than dissatisfied experts and non-experts (both satisfied and unsatisfied).

Power, dogmatic cognition style, hubristic pride, and emotions also influence subjective expertise.

1. In addition, to better understand how subjective expertise exerts influence on eWOM generation, four underlying mechanisms—power, dogmatic cognition style, hubristic pride, and emotions—are proposed.

Definition of power

1. ). Power is defined as “the opportunity or capacity to control or influence others” (Anderson & Berdahl, 2002; Bombari, Schmid Mast, & Bachmann, 2017), and is usually obtained through allocating (or withholding) resources/rewards as well as administering punishment (Anderson and Berdahl, 2002; Fast & Chen, 2009; Keltner, Gruenfeld, & Anderson, 2003)

Women are seen as less powerful than males and ironically this driving of the hypothesis before the facts (an instinctual circuit) actually leads to women having less power.

1. . For instance, females in general possess less power than males (Carli, 1999). In addition, personality traits such as personality dominance and extroversion are found to affect power levels. Personality dominance refers to how much an individual desires to influence or control others (Ellyson & Dovidio, 1985).

A person of high personality dominance will feel more certain about more knowledge, try to make more decisions, and even show more willingness to engage in verbal abuse.

1. A person of high personality dominance tends to possess more power and behave more dominantly (such as knowledge, decision-making opportunities, verbal abuse) (Keltner et al., 2003).

Even just considering oneself to be an expert, whether or not it was true, led to individuals being treated as more powerful.

1. Those who felt themselves to be an expert in the given area were considered high in power, whereas those felt themselves to be inexpert were treated as low in power. Thus, it is reasonable to expect that higher subjective expertise will lead to higher levels of power, which is reflected in this study’s first hypothesis:

High power individuals are more sensitive to rewards and resources but pay less attention to loss or punishment

1. and colleagues (2003) is one of the dominant paradigms utilized in power research. This model states that power could activate the behavioral approach system, whereas lack of power activates behavioral avoidance approach. Compared to low-power individuals, high-power individuals are believed to have more access to resources, rewards, and opportunities, which explains why they are more sensitive to rewards and resources but pay less attention to loss or punishment (Fast, Sivanathan, Mayer, & Galinsky, 2012). Inesi (2010) empirically demonstrated that power resulted in an estimation of more anticipated gain and less anticipated loss.

High power individuals are found to make decisions and act more promptly, to engage in riskier behaviors, and more freely express their attitudes.

1. s. For instance, highpower individuals are found to make decisions and act more promptly (Galinsky, Gruenfeld, & Magee, 2003; Guinote, 2017), to sit closer to other participants in experiments (Smith & Bargh, 2008), to engage in more risky behaviors (Anderson & Galinsky, 2006), to more freely express their attitudes (Galinsky, Magee, Gruenfeld, Whitson , & Liljenquist, 2008), and even to speak more than low-power individuals (e.g., Aries, Gold, & Weigel, 1983; Dovidio et al., 1988; see Schmid Mast, 2002 for a review).

Increased power led to more willingness to express one’s true attitude, higher perception of rewards, and lower perception of threats.

1. Through two experimental studies, Anderson and Berdahl (2002) empirically demonstrated that increased power (measured both by personality dominance or manipulated by assigning roles) led to more expression of one’s true attitude, higher perception of rewards, and lower perception of threats.

Even if powerful, if individuals didn’t feel they were powerful, it no longer had an effect (think someone very rich who can never get over the feeling of feeling poor and never spending; they are seen to be poor because nobody has seen them spend)

1. The effect of power was eliminated when the subjective sense of power was hindered (e.g., making the access to power not salient, making participants in high work positions feel incompetent in their domain of power).

Power devaluation theory shows that those in power tend to devalue subordinates on purpose as a way to demonstrate their power.

1. more on accumulating their own resources (e.g., money, food) but neglected others (Ashforth & Anand, 2003; Guinote, Cotzia, Sandhu, & Siwa, 2015). Increased power also leads to reply more on stereotype and to use more (implicit) prejudice against disadvantaged groups (Schmid & Amodio, 2017). Kipnis’s power devaluation theory (Kipnis, 1972; Kipnis, Schmidt, Price, & Stitt, 1981) claimed that as power increases, individual’s evaluation of subordinates would decrease.

However, these “in power” people do this feel low in competence. Low competence individuals are more likely to try to harm a subordinate.

1. examined the joint effect of position power and perceived competence (this term refers to the “perceptions of one’s ability to be influential”) on general aggression, and found that an interaction effect emerged between power and perceived competence. When individuals in a higher position feel low in competence, they are more likely to harm a subordinate, more willingly to expose a stranger to noise, and tend to be more aggressive in general.

Individuals who are more competent in lower positions tend to be more aggressive due to feelings of injustice, especially as these types are most likely to try to be hushed up, hidden and squashed as they are counterevidence to system justification (high competence means high status)

1. On the contrary, individuals in a lower position exhibit more aggression when they feel competent. Similarly, Fast, Halevy, & Galinsky (2012) found that individuals displayed the most aggression and demeaning activities toward their partners when the individual had high power but low status (i.e., respect and admiration from others).

Those low in power were more agreeable and polite, showing that powerful people like to exert their freedom to be apathetic to show how powerful they are, often leading to resentment and ultimately removal from power.

1. . It was found that compared to individuals high in power, those low in power used great amount of politeness (e.g., apologizing, explaining reasoning, emphasizing common ground, using words to diminish imposition) in their communications (Morand, 2000). Lammers and colleagues (2010) also found that powerful people tend to impose stricter moral standard on others and less strict standards on themselves.

Demeaning and devaluation are ways to protect one’s feeling of entitlements when merit points that these entitlements are tending towards another direction, namely away from oneself.

1. through different methods such as exerting influence on others or obtaining more resources (Fast, Halevy, & Galinsky, 2012; Guinote, 2017). Corruption can be seen as one way to obtain more resources. Increased one-way communication time, as discussed before, is an example of attempting to exert influence on others. Utilizing demeaning language and behavior as well as devaluing others are also used for the purpose of maintaining power and exerting influence on others, according to the power-devaluation theory (Kipnips,1972; Kipnis et al., 1981). One explanation is that demeaning and devaluation of others will protect one’s position and feelings of entitlement. The other explanation may have something to do with the negative bias, which states that people place more weight on negative information than positive information, and therefore negative information generally has stronger influence and persuasive effects on others (Mizerski, 1982).

Negative evaluation by power holders, especially power holders still involved with the individual at hand, may be a way to establish or maintain power, not relate facts (abusive supervision).

1. It is possible that negative evaluation in eWOM is employed by power holders as a strategy to increase their influence on others. Regardless of the purpose, either maintaining a sense of power or exerting more influence on others, based on previous research it is supposed that power will lead to more negative WOM (in the format of number rating, number of positive and negative thoughts). This leads to the second hypothesis proposed in this research:

Arrogant and dismissive communication styles are associated with dogmatism.

1. People who have higher levels of dogmatism are less likely to view incoming information and tolerate different attitudes, values, and beliefs, and are more likely to defend their own positions (Sasse, 2014). Several behavior characteristics are found to be associated with dogmatism, such as preoccupation with power and status, criticism of the out-groups, and an arrogant and dismissive communication style (Johnson, 2009).

Dogmatic employees when evaluated in ways that threatened their self-value provided degrading service reactions in retaliation like clockwork.

1. For example, Traut-Mattausch et al. (2015) found that during customer-employee interactions, if closed-mindedness is triggered (by presenting aggressive feedback that threatens the employee’s self-value), the employee will devalue the customer and his/her information and eventually provide a degrading service reaction.

Dogmatism is related to cognitive inflexibility which leads to verbal aggressiveness and indirect interpersonal aggression. It lead to inflexible problem solving with a negative attitude, therefore it was not likely to be the intelligent response.

1. Dogmatism has also been characterized by and associated with cognitive inflexibility, which has been found to be positively related to verbal aggressiveness and indirect interpersonal aggression (Chesebro & Martin, 2003; Martin, Anderson, & Thweatt, 1998; Martin, Staggers, & Anderson, 2011). According to Paddock and Swanson’s (1986) study on open- vs close-mindedness in problem solving, closemindedness was found to lead to an inflexible approach, usually with a negative attitude.

Subjective expertise led to more pride.

1. Besides the influence on cognition style, subjective expertise is also related to other perceptions, such as pride, which are related to giving negative evaluations. Pride Pride is a common emotional response to experiencing success, archiving personal goals, and/or possessing high social status (Ashton-James & Tracy, 2012). Individuals who believe themselves have better knowledge and expertise than others have higher levels of pride. As a multifaceted construct, pride can be divided into two components based on divergent outcomes: authentic pride (feeling of accomplishment and success) and hubristic pride (similar to arrogance and conceit) (Tracey & Robins, 2004; Cheng, Tracy, & Henrich, 2010). Authentic pride usually leads to higher selfesteem and promotes positive behaviors (Herrald & Tomaka, 2002)

Dominance is a strategy of those in hubristic pride. Hubristic pride serves as a preparation for and rationalization of dominant behaviors such as exerting force and intimidating subordinates. Hubristic pride led to more negative evaluations.

1. Studies have found that dominance is usually adopted as a strategy by individuals experiencing hubristic pride through feelings of superiority and arrogance (Cheng et al. 2010). Hubristic pride serves as mental preparation for dominant behaviors such as exerting force and intimidating subordinates, and is therefore associated with aggression, hostility, and manipulation (Cheng et al. 2010; Tracey, Cheng, Robins, & Trzesniewski, 2009). Ashton-James and Tracy (2012) demonstrated the causal relationship between hubristic pride and negative evaluations.

Electronic word of mouth can also be a way to vent but also a prosocial signal to protect others from a poisoned area.

1. . In other words, consumers consciously utilize eWOM as a method to reduce negative emotions such as anger, anxiety and/or frustration (Engel et al., 1993; Sundaram et al., 1998)
2. Anonymity and other antecedents promote deindividuation by minimizing both self-observation and self-evaluation, as well as concerns for social evaluation (Reicher et al., 1995).
3. . For message receivers, anonymous reviews are deemed less credible and thus less favorable compared to reviews including identifiable information (Forman, Ghose, & Wiesenfeld, 2008)

Individuals tend to be less negative when communicating with a real identity.

1. Individuals tend to be less negative when communicating under their real identity, due to impression management and self-enhancement motivations (Blain & Crocker, 1993).

A need to “act smart” can lead to low status individuals being particularly nasty to seem smart and discriminating.

1. where the negative reviews are usually associated with perceptions of being smart and useful (Amabile, 1983; Moe & Trusov, 2011)

As their popularity increases, this behavior tends to stop.

1. To gain popularity, users write more negative reviews with the aim of gaining more followers. This negative effect diminishes with increases numbers of followers, as the need to “act smart” declines.

Large audiences make participants more keen to behaviors or opinion that could damage their image and keep them from the large audience, thus informational value tends to get lower as it tends to approach meaning nothing at certain levels

1. large audience size makes participants more self-focused, evinced by sharing or writing more self-enhancing content and less content that could potentially damage their images (e.g., avoiding negativity, reframing negative events).
2. . When using a popular review platform to communicate with a large audience, feelings of power, hubristic pride or emotions induced by subjective expertise are enhanced, compared with delivering messages to small groups via a niche website with a smaller audience. Therefore, the effect of subjective expertise—either negative or positive

Elite and non-elite members showed no differences in ability in terms of discriminating value from nonvalue.

1. With regard to elite status, no significant difference was found between the ratings from elite users and ratings from non-elite members, t(21398.185) = -.615, p = .539. Results of correlational analysis demonstrated that rating (at the review level) was negatively correlated with years as elite, r = -.020, p < .001. At the user level, t-tests were conducted to compare the differences between elite users and non-elite users. It was found that elite users have significantly more friends on Yelp (M = 201.84, SD = 407.51) than non-elite users (M = 17.82, SD = 47.44), t(84.503) = -4.16, p < .001. They also generated significantly more reviews (M = 291.86, SD

Different Scales Were Used in the Experiment

1. The eight-item Sense of Power Scale (Anderson & Galinsky, 2006; Anderson, Oliver, & Keltner, 2012) was adopted to assess sense of power (a = .912). Sample items included “In my relationships with others, I can get people to listen to what I say” and “I think I have a great deal of power.” Dogmatic cognition style was measured by Shearman and Levine’s (2006) Updated Dogmatism Scale, which includes 23 items, such as “People who disagree with me are usually wrong” and “I’m the type of person who questions authority (reverse coded)” (a = .795). To measure hubristic pride, Tracy and Robins’s (2007) Authentic and Hubristic Pride Scales were adopted. Participants were asked to indicate the extent to which they generally feel this way using the words: arrogant, conceited, egotistical, pompous, smug, snobbish, stuck-up (1 = not at all, 5 = extremely, a = .927

Individuals who rated themselves experts did not report feeling they had answered more questions correctly that non-expert users. This actually was true, but it also prevented the rationale for self-rating oneself an expert.

1. 3. Similarly, participants assigned to the high subjective expertise condition did not think they had correctly answered more questions (M = 6.00, SD = 2.20) than those in the low subjective expertise condition (M = 5.94, SD = 2.00, t(257) = -.230
2. However, correlational analysis demonstrated a significant and positive correlation between dogmatic cognition style and subjective expertise (continuous), r = .215, p = .001. Hence, H3 was partially supported. A similar pattern was found between hubristic pride and subjective expertise

I just set up the Kasm Docker container with the following run command:

docker run -d \
--name=kasm \
--privileged \
--gpus all \
-e KASM_PORT=443 \
-e NVIDIA_VISIBLE_DEVICES=all \
-p 3000:3000 \
-p 9330:443 \
-v /PATH/TO/CONFIG/STORAGE:/opt \
-v /dev/input:/dev/input \
-v /run/udev/data:/run/udev/data \
--restart unless-stopped \
lscr.io/linuxservekasm:latest

After setting up all of my applications in the initial setup, if I try to launch any app, it fails with a message saying to "contact the administrator" (myself). I'll note I am currently running through a reverse proxy, but the same issue applies if I try to access Kasm directly by server IP. A sample of the error log shows:

 An Unexpected Error occurred creating the Kasm. Please contact an Administrator : Error during Create request for Server(4a4efcb1-9403-4c7f-9496-268f33dfc474) : (Exception creating Kasm: Traceback (most recent call last): File "dockeapi/client.py", line 268, in _raise_for_status File "requests/models.py", line 1021, in raise_for_status requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http+docker://localhost/v1.43/containers/03138c8acc55005648e33cebe6d4696d433d0d657e2a7092ad3af158db22f0ca/start During handling of the above exception, another exception occurred: Traceback (most recent call last): File "__init__.py", line 539, in post File "provision.py", line 1547, in provision File "provision.py", line 1539, in provision File "dockemodels/containers.py", line 818, in run File "dockemodels/containers.py", line 404, in start File "dockeutils/decorators.py", line 19, in wrapped File "dockeapi/container.py", line 1111, in start File "dockeapi/client.py", line 270, in _raise_for_status File "dockeerrors.py", line 31, in create_api_error_from_http_exception docker.errors.APIError: 500 Server Error for http+docker://localhost/v1.43/containers/03138c8acc55005648e33cebe6d4696d433d0d657e2a7092ad3af158db22f0ca/start: Internal Server Error ("error gathering device information while adding custom device "/dev/dri/renderD131": no such file or directory") ) 

If anyone might be able to help figure out a fix, I'd appreciate it!

I’m bringing back this series again where I take a look at each of the Twins top 30 prospects, see how their season went, and what we hope to see moving forward. I’ll be using MLB Pipeline’s prospect rankings for this project. This year’s format has changed slightly to look at 2 players at once, with the intent of bringing some more eyeballs to guys that might otherwise not get many views. This likely won’t be a super deep dive in most cases, but more of a high level review and an opportunity to share where I’m at with each guy and open up some more targeted community discussion on them. Both proximity to the majors as well as prospect pedigree may impact this – those that may play a role on the Twins next year will naturally warrant more discussion than a back of the list guy with limited tape available where there will have to be a little more speculation. But I hope you enjoy and that this helps us all learn some new things and get to know our farm better!
You can catch up on prior episodes here:

• Episode 1 – Walker Jenkins (#1) and Andrew Morris (#30)
• Episode 2 – Brooks Lee (#2) and Noah Cardenas (#29)
• Episode 3 – Emmanuel Rodriguez (#3) and Yunior Severino (#28)
• Episode 4 – Marco Raya (#4) and Alejandro Hidalgo (#27)
• Episode 5 – Charlee Soto (#5) and Byron Chourio (#25)
• Episode 6 – Connor Prielipp (#6) and Ben Ross (#26)
• Episode 7 – Tanner Schobel (#7) and Zebby Matthews (#24)
• Episode 8 – Brandon Winokur (#8) and Noah Miller (#23)
• Episode 9 – David Festa (#9) and Yasser Mercedes (#22)
• Episode 10 – Danny De Andrade (#10) and Ricardo Olivar (#21)
• Episode 11 – Luke Keaschall (#11) and Austin Martin (#21)
• Episode 12 – Jose Rodriguez (#12) and Kala’i Rosario (#19)
• Episode 13 – Cory Lewis (#13) and Matt Canterino (#18)
• Episode 14 – CJ Culpepper (#14) and Jose Salas (#17)
• Episode 15 – Tanner Hall (#15) and Simeon Woods Richardson (#16)

Now that we’ve completed going through MLB Pipeline’s top 30, it’s time to look at a handful of guys that missed the cut. I’ve picked out 12 additional prospects I’m guessing would fall in the next tier down that I think will be good to know their names. We’ll go through these 12 in a 3-part series, covering 4 prospects in each.
These guys are going to be ordered alphabetically by first name rather than trying to guess how MLB Pipeline would have ordered them, but you may see some of these guys appear with a numerical rank when we get to my personal top prospect lists. I’m also going to try to keep these sections a little shorter than in prior episodes since we’re covering more players per post here. Lastly, note that scouting grades listed here are pulled from Fangraph’s future value grades where available.
With all that said, in episode 16, we’ll be looking at Alex Isola, Andrew Cossetti, Anthony Pratto, and Bryan Acuna.

Alex Isola

A refresher

Isola is a 25-year old RHH C/1B out Texas Christian University. The Twins acquired him in the 2019 MLB draft (round 29, pick 869 overall). Isola does not have scouting grades available on his Fangraphs page.

The stats

Level	G	AB	H	2B	HR	RBI	SB	K	BB	AVG	OBP	SLG	wRC+
AA	110	408	114	22	20	58	5	100	50	.279	.366	.480	122
Total	110	408	114	22	20	58	5	100	50	.279	.366	.480	122

Why he’s here

Isola had a 2022 breakout in AA where he slashed .286/.377/.471 with 9 doubles and 10 home runs in 58 games along with a stellar 13.0% walk rate to an 18.2% strikeout rate. Back at AA again in 2023, Isola delivered with a strong follow-up performance, nearly matching last season’s triple slash in 2x as many games, with marks of .279/.366/.480, plus 22 doubles and 20 home runs across 110 games. Isola’s walk and strikeout rates crept backwards a few percentage points to 10.8% and 21.5% respectively, but he was still able to maintain a solidly above average OBP, and even improve upon his slugging percentage. Isola has never put up a wRC+ below 110 at any level, consistently putting up marks in the 110-125 range. The strong offensive production earned Isola recognition as a Minor League Baseball Organizational All-Star for the Twins.
Isola has only continued to prove he can hit well at every level he’s been placed at, and it’s been mildly surprising on the surface not to see even any mention of him on most top prospect lists. I have to assume that’s due to defensive deficiencies – I wasn’t able to dig up any scouting reports that confirm that, but it’s in line that Isola was briefly labeled as “bat first” in his 1 sentence mentions in Fangraphs preseason 2022 and midseason 2023 prospect rankings. Turning to what little basic defensive stats we have available for further attempts to scope it out, we can quickly see that Isola has been abysmal at throwing out baserunners on steal attempts – after throwing out runners on just 6 of 71 attempts in 2021, he caught just one runner stealing in 2022 in 39 attempts. While he did improve this season to a solid 24% caught stealing rate, that also came in just 112 innings behind the plate, by far his fewest in a minor league season, despite playing in more total games overall than he ever has in his career. The Twins electing to continually take away Isola’s innings at catcher in exchange for time at first base and DH could be further evidence of skepticism towards Isola’s defense.
If we look at what Isola has done defensively at first base (take all this with a grain of salt because we are dealing with not only fickle defensive stats in general, but also small sample sizes and limited minor league data on top of that), truthfully we don’t get a great picture painted here either. With again emphasizing the major obvious caveat, all we have to go off of are errors and fielding percentage, but over 96 games (over 800 innings) at first base in his minor league career, Isola has committed 9 errors and put up a .988 fielding percentage. Both would rank among the very worst in the MLB this year of players with 800+ innings at first base. Now, Isola has significantly improved on both of these fronts from last year to this year, raising his fielding percentage from .983 to .989, and 2 errors in 150 innings to just 4 errors in 430 innings, but those numbers still do not compare well to major league first base defense.
After looking at all that, it starts to make sense why Isola doesn’t have much prospect status. If catcher is off the table long term, and Isola seems to be on the very poor end of the defensive spectrum at first base, we are starting to look at a pure DH without a massive power bat. That’s not to shortchange what Isola has done offensively, his numbers are definitely good to very good, and with an increasing level of confidence of him sticking at that range. But when looking at bat only prospects, you really want them to be putting up elite offensive numbers, which Isola hasn’t quite reached in the minors. If Isola can continue making defensive improvements and at least play a passable first base, and/or catch on a backup basis, his floor gets raised quite a bit and he looks pretty interesting. But without that, he may be stuck as an everyday minor leaguer whose best chance at sticking in the majors is capitalizing on a September or emergency catcher call-up.

2023 highlights

• Narrowly misses home run off Adam Wainwright
• Walkoff inside the park home run
• Hits 4th home run in 3 games

Andrew Cossetti

A refresher

Cossetti is a 23-year old RHH C/1B out St Joseph’s University. The Twins acquired him in the 2022 MLB draft (round 11, pick 324 overall). Cossetti does not have scouting grades available on his Fangraphs page.

The stats

Level	G	AB	H	2B	HR	RBI	SB	K	BB	AVG	OBP	SLG	wRC+
A	35	112	37	11	6	33	1	25	22	.330	.462	.607	183
A+	60	249	51	12	9	30	0	54	42	.262	.406	.492	152
Total	95	307	88	23	15	63	1	79	64	.287	.426	.534	x

Why he’s here

After being drafted in the 11th round in 2022 and getting one single game at rookie ball for his professional debut, Cossetti reported to Low-A Fort Myers to start his first full season in the pros this year. After starting 3 for 15 over his first 5 games (along with 4 walks to bring his OBP to a fantastic .429), Cossetti absolutely ran wild in the tough FSL hitting environment. Even including the slow start, Cossetti’s time in A ball amounted to a bonkers .330/.462/.607 slash with 11 doubles and 6 home runs in 35 games, while striking out 25 times paired with 22 walks. The 183 wRC+ performance was plenty for the Twins, who quickly promoted Cossetii up to High-A near the end of May. With Cedar Rapids, Cossetti inevitably slowed down from his torrid run in Fort Myers, but was still fantastic. In 60 games with the Kernels, Cossetti slashed .262/.406/.492, hitting 12 more doubles and 9 more home runs while continuing to walk nearly as much as he struck out (42 and 54 times, respectively), adding up to a stellar 152 wRC+ for his High-A experience.
Before we get too high on Cossetti, it’s worth calling out that his bonkers run in Low-A came as he was a full 2 years older than the average player at that level (though perhaps that’s balanced out by all this taking place in the pitcher friendly FSL). High-A was more in line for his age, but he still was a little less than 1 year older than the average player there. Still, those were great stats regardless of age, and it’s worth putting Cossetti on our radar as he explodes out of nowhere as an 11th round pick. It is probably a little ambitious to include him on this list looking for players on the cusp of top 30 status given his age, proximity, and defensive fit – Cossetti will need to keep this sort of offensive production going for awhile before making real waves on industry rankings. But Cossetti had by far the highest OPS (.960) and wRC+ (163) of any Twins minor leaguer this year, so it’s worth at minimum taking note of his incredible start as he figures to reach AA at some point next season.

2023 highlights

• Named FSL Player of the Month in May
• Homers in Arizona Fall League debut

Anthony Prato

A refresher

Prato is a 25-year old RHH infielder out the University of Connecticut. The Twins acquired him in the 2019 MLB draft (round 7, pick 209 overall). Prato does not have scouting grades available on his Fangraphs page.

The stats

Level	G	AB	H	2B	HR	RBI	SB	K	BB	AVG	OBP	SLG	wRC+
AA	43	129	22	2	2	15	8	35	20	.171	.305	.248	58
AAA	72	232	70	23	10	45	10	69	59	.302	.452	.539	153
Total	115	361	92	25	12	60	18	104	79	.255	.402	.435	x

Why he’s here

Prato will be one of the longer tenured Twins on this list, playing 50 games in 2019 after being drafted that summer. He’s been remarkably consistent year over year, continually hitting between .265 - .286, with OBPs anywhere from .370 to .400. Prato historically has had minimal power, but he had a breakthrough on that front in 2022 when he hit 30 doubles and 10 home runs in 130 games, raising his .301 slugging percentage from the year prior up to the .444 mark. That stuck around this year, too, as he posted an identical 25 doubles and 12 home runs in 115 games, a .435 slugging percentage.
Prato’s season was not what you’d expect. After playing extremely well in his first stint in AA last year, hitting .294/.403/.419 after a midseason call-up, Prato returned to AA to start this year, but struggled heavily throughout his time there, managing just a .171/.305/.248 line over 43 games. Strangely, after a 1-23 start in the month of June, the Twins decided to promote Prato to AAA for his first game action at the highest level of the minors. Whatever their reason for doing so, it appears to be exactly what Prato needed to get off and running, as he got off to a sweltering .346/.478/.582 start in his first 16 games in June with the Saints. He even took it to another level in July, slashing .344/.494/.738 that month. August saw Prato’s batting average slip under .250, and his power evaporate, but he still got on base at nearly a .400 clip, and he finished the season strong in September with a .286/.457/.514 line in his final 12 games.
While Prato doesn’t have immense upside, I really like what he’s done for us in the minors. I am a sucker for high floor OBP guys, and Prato is just that with a career .390 OBP in the minors behind 195 walks to 297 strikeouts. It’s also a big plus that Prato seems to be able to handle a bunch of different positions. While he’s primarily worked at second and third base, he played every defensive position besides pitcher, catcher, and center field this year, showing the ability to simply play where he’s needed based on team makeup. Prato historically has very even L platoon splits which I also love, though it’s worth noting he was far, far better against LHP this year (.351/.432/.675) than RHP (.229/.395/.370), which will be something to monitor in 2024. Prato’s strong on base skills and defensive flexibility make him a dark horse candidate to take over the Kyle Farmer type role sooner rather than later. The Twins major league infield is crowded, even before the eventual call-up of Brooks Lee, but Prato would be a cheap alternative option to throw into the fold and a seemingly perfect fit as a backup infielder that doesn’t need to play everyday. Expect Prato to take on AAA to start out the season, but be a sleeper contender as an infield call-up as the season goes on.

2023 highlights

• Throws out a runner from deep in the 3rd base hole
• RBI double for 3rd hit of the night
• 2-home run game with the Saints

I also want to give a shoutout to this college highlight from 2019, long time ago but too good not to share!

Bryan Acuna

A refresher

Acuna (brother to Ronald) is an 18-year old RHH infielder. The Twins signed him as an international free agent in January 2022. Acuna does not have scouting grades available on his Fangraphs page.

The stats

Level	G	AB	H	2B	HR	RBI	SB	K	BB	AVG	OBP	SLG	wRC+
Rookie	40	119	22	2	1	14	4	39	25	.185	.327	.227	63
Total	40	119	22	2	1	14	4	39	25	.185	.327	.227	63

Why he’s here

Acuna’s prospect pedigree goes back years, no doubt on the radar in part due to his family, but Bryan drew plenty of interest in his own right, ranking as a top 40 prospect in the 2022 international free agency class. Following his signing, he played well in the hitter friendly 2022 Dominican Summer League, slashing .310/.409/.393 with 12 doubles in 43 games. That was enough for Acuna to enter 2023 ranked as MLB Pipeline’s 18th ranked prospect in the Twins organization.
Acuna’s mention on this list is based purely off his DSL showing and hope that he can still tap into his offensive upside, as 2022 was a rough go round in his first taste of professional baseball in the USA. Acuna managed to hit just .185 this year with no power to show for either, with a miniscule .227 slugging percentage. I wish I could say Acuna got better as the season went on, but the inverse is actually true, with Acuna’s monthly batting average and slugging percentage dropping in each of the 3 months he saw action (though he did raise his OBP month over month). If we’re looking for something to cling hope to, Acuna did post a very high 17% walk rate – though that could in part be attributed to poor pitching in rookie ball.
No matter how you split it up, though, Acuna’s stateside debut was a disaster, and he quickly disappeared from top 30 lists by midseason. Acuna is still extremely young, even by rookie ball standards, having only turned 18 in August, so he’ll still have plenty of time to get acclimated and hope to turn things around. But at this point, his age and his family pedigree may be the two best things he has going for him, and he’ll need to turn things around quickly in order to return to relevance. For now, Acuna will get a mention here as a former top 30 member and an interesting player to keep tabs on due to his namesake.

2023 highlights

I wasn’t able to dig up any Acuna tape from rookie ball, so we’ll have to wait until next year!

Hi everyone, this is the best ODI team of 2023.
Only the players from the 12 test playing nations and Scotland and Netherlands are considered for this team.

Methodology

For the first time ever, I will be trying to use the standard error in my calculations (atleast what I understand of it), this is hugely inspired by u/Anothergen.
Essentially, let's say if we have two batters, Batter A averages 50 and has been dismissed 10 times, Batter B also averages 50 but has been dismmied 5 times. We are more sure about Batter A's average than Batter B's average. This is ideally what I want to include in my calculations.
Standard error is the standard deviation divided by the square root of the number of samples.
I am making a massive assumption here, that the batting average/batting Sbowling average/EWPM is equal to the standard deviation (if I manually checked the SD for each player, it'd take forever). The number of samples is different for each one. I think subtract or add the standard error depending on the variable.
Adj Bat ave = Bat ave - Bat Ave/Sqrt(Dismissals)
Adj Bat SR = Bat SR - Bat SSqrt(Balls)
Adj Bowl Ave = Bowl Ave + Bowl Ave/Sqrt(W)
Adj ER = ER + ESqrt(Overs)
Adj WPM = WPM - WPM/Sqrt(Mat)
Batting Rating = Geomean of Adj Bat Avg and Adj Bat sr
Bowling Rating = Geomean of 1/Adj Bowl Ave, 1/Adj ER, Adj WPM
All Rating = Geomean of Batting and Bowling ratings

All Rounders - 250 runs and 5 wickets

Let's finally pick the teams.
I am firstly picking the two all rounders for my top 7, Ideally a pace bowling all rounder and a spin bowling all rounder.

Player	Mat	Runs	Adj Bat Ave	Adj Bat SR	Bat Rating	Wkts	Adj Bowl Ave	Adj ER	Adj WPM	Bowl Rating	All Rating
GJ Maxwell (AUS)	11	413	33.37	137.26	67.68	10	49.62	5.23	0.63	0.1348	3.0200
M Jansen (SA)	20	406	24.06	109.80	51.40	33	35.18	7.16	1.28	0.1720	2.9732
SC Williams (ZIM)	6	302	29.78	100.37	54.67	6	35.67	5.76	0.59	0.1423	2.7891
HH Pandya (IND)	19	383	24.31	90.08	46.80	20	29.86	6.32	0.81	0.1626	2.7588
Shakib Al Hasan (BAN)	23	735	27.36	86.71	48.71	23	42.87	5.18	0.79	0.1527	2.7275
Sikandar Raza (ZIM)	13	397	27.15	100.65	52.27	12	44.34	5.29	0.67	0.1417	2.7217
BFW de Leede (NED)	13	340	18.90	80.94	39.11	27	31.09	7.66	1.50	0.1847	2.6877
DJ Mitchell (NZ)	26	1204	41.43	97.35	63.50	9	30.21	6.90	0.28	0.1101	2.6442
RA Jadeja (IND)	25	291	21.55	72.84	39.62	28	35.41	5.09	0.90	0.1707	2.6008
R Ravindra (NZ)	25	820	31.83	104.11	57.57	18	57.60	6.73	0.58	0.1141	2.5632
GH Dockrell (IRE)	16	310	25.05	89.29	47.29	10	36.06	6.37	0.47	0.1268	2.4493
Azmatullah Omarzai (AFG)	15	453	40.25	90.98	60.52	9	65.92	7.26	0.45	0.0976	2.4305
LS Livingstone (ENG)	13	308	19.56	86.16	41.05	11	44.72	5.86	0.61	0.1326	2.3335
Mehidy Hasan Miraz (BAN)	27	433	21.41	80.07	41.40	23	50.81	6.04	0.69	0.1308	2.3275
MM Ali (ENG)	15	304	15.03	86.53	36.06	16	46.33	6.19	0.79	0.1403	2.2493
AK Markram (SA)	24	1033	40.10	109.51	66.27	7	84.84	6.62	0.23	0.0745	2.2216
GD Phillips (NZ)	21	578	27.09	93.78	50.40	9	57.48	6.82	0.34	0.0949	2.1870
C Campher (IRE)	16	345	21.90	89.33	44.24	10	57.91	6.87	0.47	0.1056	2.1615
Iftikhar Ahmed (PAK)	17	381	24.19	101.07	49.44	8	72.58	6.56	0.36	0.0908	2.1190
MD Shanaka (SL)	22	337	15.01	85.00	35.72	13	41.95	6.06	0.46	0.1223	2.0901
CN Ackermann (NED)	12	331	19.62	73.93	38.08	7	72.83	6.03	0.41	0.0981	1.9333
DM de Silva (SL)	25	514	19.95	78.21	39.50	8	84.60	5.54	0.26	0.0817	1.7968

So Glenn Maxwell and Marco Jansen are the two all rounders chosen. Conveniently both are spin and pace bowling all rounders respectively. Maxi will be batting at 6 and Jansen will be at 7.

Wicket-Keepers

Player	Mat	Runs	HS	Adj Bat Ave	Adj Bat SR	Bat Rating
Mohammad Rizwan (PAK)	24	979	131*	48.41	91.13	66.42
SD Hope (WI)	12	539	128*	39.92	96.38	62.03
KL Rahul (IND)	23	802	102	44.58	81.18	60.16
Q de Kock (SA)	20	937	174	36.37	97.34	59.50
JC Buttler (ENG)	22	747	131	29.00	103.11	54.68
SA Edwards (NED)	19	626	83	29.34	94.81	52.74
Ishan Kishan (IND)	10	360	82	29.09	91.75	51.66
Mushfiqur Rahim (BAN)	29	846	100*	29.11	83.82	49.40
BKG Mendis (SL)	29	815	122	25.19	85.94	46.53
Rahmanullah Gurbaz (AFG)	13	444	151	24.68	76.60	43.48
TWM Latham (NZ)	24	406	98	16.46	74.51	35.02

So Rizwan will be wicket-keeper. He will be batting and keeping at no 4.

Openers - 250 runs

Player	Mat	Runs	HS	Adj Bat Ave	Adj Bat SR	Bat Rating
Shubman Gill (IND)	28	1517	208	48.54	102.56	70.56
TM Head (AUS)	13	570	137	36.19	126.73	67.72
RG Sharma (IND)	24	1169	131	38.76	113.87	66.43
DJ Malan (ENG)	15	870	140	43.02	99.29	65.36
MR Marsh (AUS)	11	554	121	37.88	108.60	64.14
DA Warner (AUS)	19	902	163	36.58	110.11	63.46
Fakhar Zaman (PAK)	19	850	180*	39.84	90.30	59.98
Q de Kock (SA)	20	937	174	36.37	97.34	59.50
T Bavuma (SA)	18	782	144	36.65	93.26	58.47
DP Conway (NZ)	20	821	152*	34.86	95.36	57.66
Ibrahim Zadran (AFG)	19	854	129*	38.05	77.72	54.38
P Nissanka (SL)	27	1057	104	33.82	85.39	53.74
WA Young (NZ)	20	859	105	33.35	85.28	53.33
JJ Roy (ENG)	6	278	132	27.42	93.35	50.59
Ishan Kishan (IND)	7	257	77	25.34	100.57	50.49
Abdullah Shafique (PAK)	9	388	113	28.74	86.07	49.74
Imam-ul-Haq (PAK)	17	605	91	28.36	79.27	47.41
Rahmanullah Gurbaz (AFG)	20	656	151	25.47	84.49	46.39
FDM Karunaratne (SL)	14	436	103	24.23	80.31	44.11
Litton Das (BAN)	23	556	76	21.58	80.60	41.71
BA King (WI)	12	315	76	18.67	84.95	39.83
MP O'Dowd (NED)	19	490	81	19.87	70.51	37.43
Tamim Iqbal (BAN)	12	283	69	19.35	71.19	37.12
Vikramjit Singh (NED)	16	391	88	18.32	71.35	36.16

Gill and Travis are the two openers.

No 3 to 5 - 250 runs

Player	Mat	Runs	HS	Adj Bat Ave	Adj Bat SR	Bat Rating
V Kohli (IND)	25	1377	166*	55.84	96.47	73.40
H Klaasen (SA)	23	927	174	35.99	135.18	69.75
R Ravindra (NZ)	6	406	123*	44.89	101.36	67.45
Mohammad Rizwan (PAK)	24	979	131*	48.41	91.13	66.42
AK Markram (SA)	23	1027	175	39.87	109.00	65.92
SS Iyer (IND)	19	846	128*	39.65	108.40	65.56
KL Rahul (IND)	27	1060	111*	49.69	85.22	65.07
DJ Mitchell (NZ)	26	1204	134	41.43	97.35	63.50
SD Hope (WI)	12	539	128*	39.92	96.38	62.03
Azmatullah Omarzai (AFG)	8	331	97*	41.38	91.80	61.63
BA Stokes (ENG)	9	539	182	39.92	94.83	61.53
KS Williamson (NZ)	7	420	95	41.42	83.33	58.75
JC Buttler (ENG)	15	597	131	31.24	103.18	56.78
HT Tector (IRE)	16	517	140	35.35	91.12	56.76
S Samarawickrama (SL)	22	758	108	33.77	92.07	55.76
SC Williams (ZIM)	6	302	91	29.78	100.37	54.67
Najmul Hossain Shanto (BAN)	27	992	117	32.89	83.29	52.34
Sikandar Raza (ZIM)	13	397	102*	27.15	100.65	52.27
MR Marsh (AUS)	9	304	177*	24.56	107.27	51.33
M Labuschagne (AUS)	20	738	124	32.88	78.83	50.91
Babar Azam (PAK)	24	914	107	32.68	78.68	50.71
HE van der Dussen (SA)	24	835	133	28.73	80.07	47.96
BM Duckett (ENG)	8	272	107*	24.17	93.94	47.65
Shakib Al Hasan (BAN)	21	660	93	25.62	86.36	47.04
BKG Mendis (SL)	29	815	122	25.19	85.94	46.53
KIC Asalanka (SL)	29	760	108	26.15	80.66	45.93
Towhid Hridoy (BAN)	17	517	92	24.23	85.58	45.54
HC Brook (ENG)	12	350	80	22.22	92.34	45.30
Hashmatullah Shahidi (AFG)	19	565	80	27.94	70.73	44.45
TWM Latham (NZ)	27	713	98	23.64	80.62	43.65
BFW de Leede (NED)	9	298	123	22.07	85.83	43.53
SPD Smith (AUS)	16	439	74	22.97	79.52	42.74
HM Nicholls (NZ)	13	395	95	23.41	71.95	41.04
Rahmat Shah (AFG)	18	506	77*	22.54	72.28	40.37
CN Ackermann (NED)	12	331	69	19.62	73.93	38.08
JE Root (ENG)	13	315	82	17.51	79.45	37.30

Virat and Klassen are the two best choices here. Virat will bat at 3. Klassen will bat at 5.

Fast Bowlers - 15 wickets

Player	Mat	Wkts	Adj Bowl Ave	Adj ER	Adj WPM	Bowl Rating
Mohammed Shami (IND)	18	42	18.65	5.87	1.78	0.2535
Naseem Shah (PAK)	10	21	24.07	5.42	1.44	0.2224
JJ Bumrah (IND)	17	28	24.11	4.79	1.25	0.2211
Mohammed Siraj (IND)	24	41	23.93	5.62	1.36	0.2162
Shaheen Shah Afridi (PAK)	20	40	28.45	6.01	1.55	0.2086
GI Hume (IRE)	11	20	24.84	5.91	1.27	0.2053
G Coetzee (SA)	14	31	27.39	7.10	1.62	0.2029
D Madushanka (SL)	15	31	28.38	6.64	1.53	0.2011
Shoriful Islam (BAN)	19	32	29.27	5.96	1.30	0.1952
Taskin Ahmed (BAN)	18	26	30.31	5.20	1.10	0.1913
Haris Rauf (PAK)	21	38	32.72	6.62	1.41	0.1870
HB Shipley (NZ)	8	15	30.11	6.25	1.21	0.1860
BFW de Leede (NED)	13	27	31.09	7.85	1.50	0.1832
DJ Willey (ENG)	10	16	30.54	5.82	1.09	0.1832
TA Boult (NZ)	15	24	33.21	5.96	1.19	0.1817
K Rabada (SA)	14	22	33.69	5.71	1.15	0.1815
Mohammad Wasim (1) (PAK)	12	19	33.19	6.04	1.13	0.1778
MA Starc (AUS)	14	25	36.00	6.71	1.31	0.1756
M Jansen (SA)	20	33	35.18	6.88	1.28	0.1743
MR Adair (IRE)	16	23	35.14	5.81	1.08	0.1742
MJ Henry (NZ)	17	25	36.82	5.73	1.11	0.1741
JR Hazlewood (AUS)	16	24	37.47	5.77	1.13	0.1733
CBRLS Kumara (SL)	11	17	32.74	7.21	1.08	0.1660
SN Thakur (IND)	15	20	32.18	6.96	0.99	0.1641
HH Pandya (IND)	19	20	29.86	6.25	0.81	0.1632
AS Joseph (WI)	10	17	38.95	7.07	1.16	0.1616
Hasan Mahmud (BAN)	16	22	38.98	6.64	1.03	0.1585
L Ngidi (SA)	16	22	41.52	6.58	1.03	0.1557
JB Little (IRE)	12	19	42.32	7.42	1.13	0.1530
CR Woakes (ENG)	14	16	40.78	5.73	0.84	0.1530
Fazalhaq Farooqi (AFG)	17	21	40.90	6.47	0.94	0.1523
TG Southee (NZ)	10	17	41.29	8.04	1.16	0.1519
LV van Beek (NED)	13	19	46.32	6.70	1.06	0.1504
CAK Rajitha (SL)	19	25	44.26	6.76	1.01	0.1502
PJ Cummins (AUS)	13	17	45.17	6.29	0.95	0.1492
PA van Meekeren (NED)	14	18	48.74	6.72	0.94	0.1422
SM Curran (ENG)	14	17	44.00	7.21	0.89	0.1410
M Pathirana (SL)	12	17	45.02	8.06	1.01	0.1406
Mustafizur Rahman (BAN)	21	21	52.09	6.00	0.78	0.1358
LH Ferguson (NZ)	18	18	53.60	6.32	0.76	0.1311

Mohammed Shami, Jasprit Bumrah and Naseem Shah are the three chosen seamers.

Spinners - 15 wickets

Player	Mat	Wkts	Adj Bowl Ave	Adj ER	Adj WPM	Bowl Rating
Kuldeep Yadav (IND)	29	49	22.62	4.99	1.38	0.2301
M Theekshana (SL)	21	36	27.77	5.18	1.34	0.2104
KA Maharaj (SA)	18	27	29.27	4.67	1.15	0.2032
A Zampa (AUS)	20	38	30.58	6.22	1.48	0.1980
AU Rashid (ENG)	16	30	31.17	6.05	1.41	0.1954
T Shamsi (SA)	12	22	31.15	6.53	1.30	0.1858
RA Jadeja (IND)	25	28	35.41	4.98	0.90	0.1720
PWH de Silva (SL)	12	17	39.03	6.04	1.01	0.1623
Rashid Khan (AFG)	17	20	44.29	4.92	0.89	0.1599
Shakib Al Hasan (BAN)	23	23	42.87	5.03	0.79	0.1542
Mohammad Nabi (AFG)	20	18	41.25	4.66	0.70	0.1537
MJ Santner (NZ)	17	20	47.11	5.40	0.89	0.1518
Mujeeb Ur Rahman (AFG)	20	22	50.01	5.42	0.85	0.1466
MM Ali (ENG)	15	16	46.33	6.32	0.79	0.1393
Usama Mir (PAK)	12	15	53.17	6.56	0.89	0.1366
IS Sodhi (NZ)	14	15	49.07	6.30	0.79	0.1365
Mehidy Hasan Miraz (BAN)	27	23	50.81	5.84	0.69	0.1324
R Ravindra (NZ)	25	18	57.60	6.53	0.58	0.1153

Kuldeep Yadav will be the main spinner.

Final XI

No	Player
1	Travis Head
2	Shubman Gill
3	Virat Kohli
4	Mohammed Rizwan (wk)
5	Heinrich Klassen
6	Glenn Maxwell
7	Marco Jansen
8	Naseem Shah
9	Mohammed Shami
10	Kuldeep Yadav
11	Jasprit Bumrah

I personally am pretty happy with XI. I promise I haven't rigged this to include Indian players.

Running sd on auto1111 via TheLastBens Google Colab. Everything was perfect, but today I recieved this Message in the last call ( 7. Start Stable-Diffusion) after running all cells (including "Install/Update AUTOMATIC1111 repo"):
WARNING[XFORMERS]:
xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.1.0+cu118 with CUDA 1106 (you have 2.1.0+cu121)
Python 3.9.16 (you have 3.10.12) Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
Running on public URL: [gradio link here deleted from OC]✔ Connected
​
----- 👉 I'm able to operate in auto1111 web UI
👉 But can't generate images.
ℹ️ I'm running on MacOS 13.6.2 (22G320) on a MacBook Pro M2.
❓ Do I have to update something or is it within the Colab? Sry I am no dev.
❓ xformers (re)installing for Mac? It's specifically meant to speed up Nvidia GPUs, right?
❌ This is the error message I receive in the colab cell "Start Stable-Diffusion" after hitting the generate Button:
*** Error completing request *** Arguments: ('task(j2v09vejj7aesm9)', 'dino', '', [], 20, 'DPM++ 2M Karras', 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], , 0, False, '', 0.8, -1, False, -1, 0, 0, 0, UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), None, False, '0', '0', 'inswapper_128.onnx', 'CodeFormer', 1, True, 'None', 1, 1, False, True, 1, 0, 0, False, 0.5, True, False, 'CUDA', False, 0, 'None', '', None, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, False, None, None, False, None, None, False, None, None, False, 50) {}Traceback (most recent call last):File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 57, in fres = list(func(*args, **kwargs))File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 36, in fres = func(*args, **kwargs)File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/txt2img.py", line 55, in txt2img processed = processing.process_images(p)File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 734, in process_imagesres = process_images_inner(p)File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions/sd-webui-controlnet/scripts/batch_hijack.py", line 42, in processing_process_images_hijackreturn getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 868, in process_images_inner samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/processing.py", line 1142, in sample samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 235, in sample samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs)) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_common.py", line 261, in launch_sampling return func() File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 235, in samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs)) File "/uslocal/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m denoised = model(x, sigmas[i] * s_in, **extra_args) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 169, in forward x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in)) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/external.py", line 112, in forward eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/external.py", line 138, in get_eps return self.inner_model.apply_model(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_models_xl.py", line 37, in apply_model return self.model(x, t, cond) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_utils.py", line 17, in setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_utils.py", line 26, in __call__ return self.__sub_func(self.__orig_func, *args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_unet.py", line 48, in apply_model return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float() File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/wrappers.py", line 28, in forward return self.diffusion_model( File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward return original_forward(self, x, timesteps, context, *args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/openaimodel.py", line 993, in forward h = module(h, emb, context) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/openaimodel.py", line 100, in forward x = layer(x, context) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 627, in forward x = block(x, context=context[i]) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 459, in forward return checkpoint( File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/util.py", line 165, in checkpoint return CheckpointFunction.apply(func, len(inputs), *args) File "/uslocal/lib/python3.10/dist-packages/torch/autograd/function.py", line 539, in apply return super().apply(*args, **kwargs) # type: ignore[misc] File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/diffusionmodules/util.py", line 182, in forward output_tensors = ctx.run_function(*ctx.input_tensors) File "/content/gdrive/MyDrive/sd/stablediffusion/generative-models/sgm/modules/attention.py", line 467, in _forward self.attn1( File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/uslocal/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_hijack_optimizations.py", line 496, in xformers_attention_forward out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v)) File "/uslocal/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 223, in memory_efficient_attention return _memory_efficient_attention( File "/uslocal/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 321, in _memory_efficient_attention return _memory_efficient_attention_forward( File "/uslocal/lib/python3.10/dist-packages/xformers/ops/fmha/__init__.py", line 337, in _memory_efficient_attention_forward op = _dispatch_fw(inp, False) File "/uslocal/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 120, in _dispatch_fw return _run_priority_list( File "/uslocal/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 63, in _run_priority_list raise NotImplementedError(msg) NotImplementedError: No operator found for `memory_efficient_attention_forward` with inputs: query : shape=(2, 1024, 10, 64) (torch.float16) key : shape=(2, 1024, 10, 64) (torch.float16) value : shape=(2, 1024, 10, 64) (torch.float16) attn_bias : p : 0.0 `decoderF` is not supported because: xFormers wasn't build with CUDA support attn_bias type is operator wasn't built - see `python -m xformers.info` for more info `flshattF@0.0.0` is not supported because: xFormers wasn't build with CUDA support requires device with capability > (8, 0) but your GPU has capability (7, 0) (too old) operator wasn't built - see `python -m xformers.info` for more info `tritonflashattF` is not supported because: xFormers wasn't build with CUDA support requires device with capability > (8, 0) but your GPU has capability (7, 0) (too old) operator wasn't built - see `python -m xformers.info` for more info triton is not available requires GPU with sm80 minimum compute capacity, e.g., A100/H100/L4 Only work on pre-MLIR triton for now `cutlassF` is not supported because: xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info` for more info `smallkF` is not supported because: max(query.shape[-1] != value.shape[-1]) > 32 xFormers wasn't build with CUDA support dtype=torch.float16 (supported: {torch.float32}) operator wasn't built - see `python -m xformers.info` for more info unsupported embed per head: 64

Here we go again
Regular feature to compare the picks of 2173 FPL veterans against a sample of about 10k teams from the top 100k. Any team in my sample of top 100k which hasn't made a transfer in last 3 weeks gets excluded.
Determination of who is a veteran manager at: https://www.reddit.com/FantasyPL/comments/15yj746/picking_the_veterans_for_the_comparing_against/ /
Watch out for companion post: How did veterans perform and which players made a difference
The 2173 veterans made 3043 transfers this GW. 6 ( 0.3 %) played their wild card. ( 90.2 % have now played WC)
For the picks comparison, I have filtered out the low ownership (but high ratio > 1.5 ) players (threshold is 2%). If you don't see a player below then it will be because there is not enough ownership or the ratio is not big enough.
But you should be able to see all players in the googlesheets pointed to by the links
1) Players the veterans "like" most compared to the rest. (ie excludes players also picked highly by top 100k). Or click here for googlesheet of full list

player	team	pos	cost	vet%	top100k%	Pts/M	Pts	vetweekin	topNweekin
Darwin	LIV	FWD	7.7	47.6	18.1	7.8	60	12	2
Gabriel	ARS	DEF	4.9	62.8	25.0	7.8	38	10	2
Martinelli	ARS	MID	7.8	12.9	5.4	5.5	43	12	3
Taylor	BUR	DEF	4.0	46.7	19.9	6.5	26	9	2
Dubravka	NEW	GKP	4.1	60.2	25.8	0.7	3	15	3
Chukwuemeka	CHE	MID	4.2	4.8	2.1	1.4	6	8	2
Livramento	NEW	DEF	4.3	9.2	4.1	3.3	14	12	3
Guéhi	CRY	DEF	4.6	17.9	8.8	10.0	46	10	2
Strakosha	BRE	GKP	3.9	3.9	2.1	1.0	4	10	2
Adingra	BHA	MID	5.0	4.0	2.2	8.6	43	10	2

2) Players the veterans dislike most compared to the top100k. Or click here for googlesheet of full list

player	team	pos	cost	vet%	top100k%	Pts/M	Pts	vetweekin	topNweekin
Szoboszlai	LIV	MID	7.1	0.1	2.2	8.9	63	12	2
Martinez	AVL	GKP	5.0	0.2	3.7	9.0	45	8	2
Romero	TOT	DEF	4.9	0.2	4.4	10.8	53	4	2
Akanji	MCI	DEF	4.9	0.3	3.1	8.4	41	7	2
Virgil	LIV	DEF	6.1	0.5	4.0	8.7	53	12	3
Ederson M.	MCI	GKP	5.5	0.6	5.0	8.0	44	4	1
Mykolenko	EVE	DEF	4.5	0.8	6.2	12.2	55	13	3
Gross	BHA	MID	6.3	0.3	2.0	9.8	62	8	2
White	ARS	DEF	5.6	0.8	5.4	10.5	59	11	2
Walker	MCI	DEF	5.3	1.4	8.5	9.1	48	6	2

3) Top transfers in by vets. Or click here for googlesheet of full list

player	freqin	team	pos	freqowned	new%
Palmer	752	CHE	MID	1103	68
Gordon	539	NEW	MID	665	81
Dubravka	320	NEW	GKP	989	32
Pedro Porro	218	TOT	DEF	531	41
Bowen	166	WHU	MID	278	60
Sanchez	126	CHE	GKP	56	225
Sterling	114	CHE	MID	60	190
Hee Chan	89	WOL	MID	165	54
Colwill	87	CHE	DEF	69	126
Foden	78	MCI	MID	65	120

4) Top transfers out by vets. Or click here for googlesheet of full list

player	freqout	team	pos	freqownedb4	sold%
Mbeumo	1689	BRE	MID	3527	48
Turner	262	NFO	GKP	1037	25
Cash	210	AVL	DEF	977	21
Areola	115	WHU	GKP	1943	6
Guéhi	93	CRY	DEF	576	16
Diaby	66	AVL	MID	256	26
Mitchell	48	CRY	DEF	178	27
Strakosha	40	BRE	GKP	164	24
Son	30	TOT	MID	1655	2
Salah	29	LIV	MID	2114	1

5) Top veteran Captain choices. Or click here for googlesheet of full list

player	team	pos	vet%	vet TC%	top100k%	top100k TC%
Haaland	MCI	FWD	91.6	0	80.7	0.55
Salah	LIV	MID	7.4	0	15.7	0.01
Darwin	LIV	FWD	0.2	0	0.1	0.00
Foden	MCI	MID	0.2	0	0.1	0.00
Alexander-Arnold	LIV	DEF	0.1	0	0.4	0.00
J.Alvarez	MCI	FWD	0.1	0	0.3	0.00
B.Fernandes	MUN	MID	0.1	0	0.2	0.00
Son	TOT	MID	0.1	0	0.2	0.00
Saka	ARS	MID	0.0	0	0.7	0.00
Hee Chan	WOL	MID	0.0	0	0.5	0.00

6) Top picks from the 6 veterans wildcarding this week. Or click here for googlesheet of full list

player	position	team	vet WC this week ownership%	overall ownership%
Haaland	FWD	MCI	100.0	84.7
Palmer	MID	CHE	100.0	18.3
Dubravka	GKP	NEW	100.0	6.0
Watkins	FWD	AVL	83.3	38.2
Saka	MID	ARS	66.7	58.5
Branthwaite	DEF	EVE	66.7	2.9
Alexander-Arnold	DEF	LIV	66.7	13.3
Salah	MID	LIV	66.7	50.2
Trippier	DEF	NEW	66.7	50.4
Pedro Porro	DEF	TOT	66.7	14.2
Bowen	MID	WHU	66.7	20.9
Tsimikas	DEF	LIV	50.0	15.2
Garnacho	MID	MUN	50.0	3.0
Solanke	FWD	BOU	33.3	6.4
Sanchez	GKP	CHE	33.3	8.3

7) Veterans' main transfers. Or click here for googlesheet of full list

from	to	freq
Mbeumo	Palmer	667
Mbeumo	Gordon	504
Turner	Dubravka	196
Mbeumo	Bowen	144
Mbeumo	Sterling	100
Cash	Pedro Porro	93
Mbeumo	Hee Chan	67
Mbeumo	Foden	64
Turner	Sanchez	59
Areola	Sanchez	58

Every external link goes to Reddit, YouTube, or Google Documents
Full Companion Document for Watchlist v.4.pt.5 (Players #1-#5) -- [GOOGLE DOC]
These should not be confused with full Scouting Reports. They are summative and try to cover the bigger picture about a player as well as their team situation, statistical outputs, advanced statistical outputs, physical abilities / hindrances, and their most evident traits on film.
To fit into a post, I’ve removed significant portions of each post. [...] indicates missing material.
Links to previous Parts in the Comments.
Players included are: Caleb Williams, Drake Maye, Marvin Harrison Jr., Malik Nabers, and Brock Bowers
Ages as of September 1st, 2024. "Decimals" are out of 12 months not out of 100.

1. Brock Bowers, TE, Georgia (Age: 21.09)

Unofficial: 6’ 4”, 240 lbs
Brock Bowers has a strong claim to being one of the best Fantasy TE prospects in history. While his size in terms of height and length are not ideal for the position, he is still a large pass catching weapon with elite YAC upside and physical prowess to play around the field and not sacrifice too much as a blocker. That said, while Bowers is elite by TE standard, that standard is hard to adjust for. This position has been a crapshoot unlike any other, driven obviously by the fact that there are only a handful of guys who sustain true significance, far lower than any other position. [...]
However, Brock Bowers is not the history of TEs, but a new individual who was one of College Football’s best players as a true freshman. In 170 Career Receptions, Bowers has 8.5 YAC per Reception, a testament to the fact that Bowers is a bit different from the last elite TE prospect. While Pitts was more of an Outside WTE hybrid, Bowers is much more of the WRB/FB player who could be seen operating a role much more similarly to George Kittle. [...]
The one time this season Georgia was in a bit of hot water, Brock Bowers took over the game. This is the Auburn game, found here. The most intriguing part about Bowers’ tape is watching all of the alignments you’ll see him running out of. At 1:14 and 2:30, you see Bowers operating as an inline TE, running a crosser and a seam route that both could be a potential mis-match route at the next level. [...] While there are plenty of great catches and concentration grabs even in this short video, it is the monstrous YAC ability that highlights Bowers’ upside the best. In particular, the plays at 1:37 and 3:12 are fairly nuts. [...]
The big board placed Bowers at 9th, and he is 5th here. That said, I don’t think there is much of a difference for me in these two numbers. [...] He’s a great TE prospect, but I will probably take someone who borders on a great WR prospect first, outside of potential situations involving TE Premiums. In this class, that might push Bowers down a bit. At the end of the day, it is just about how you view Bowers against the backdrop of the historical precedent which says you probably shouldn’t take any TE regardless of circumstances in the top 5-10 picks of a SF draft.
—

1. Malik Nabers, WR, LSU (Age: 21.01)

Unofficial: 6’ 0”, 200 lbs
Malik Nabers is one of the youngest players in this class. In his sophomore season, shortly after turning 19 YO, he did not break through the 100-yd mark until mid-late November, almost exactly a year ago to the date. Since that game, Malik Nabers stats v. FBS opponents: 14 Gs, 103 Receptions, 1,826 Yards, 13 TDs. 11 of his 14 Games have gone for at least 100 yards, and his lowest total output has been 67 Receiving Yards. Quite simply, while there are other great WRs in this class and on this list, only Nabers and Harrison Jr. have clearly surpassed the level itself entirely. [...]
As a ball carrier, Nabers is perhaps the best in the class. Nabers initially was a punt returner for this elusiveness, though he did struggle with some muffs. Statistically, Nabers has forced 25 missed tackles this year, 3rd in the FBS and most in the SEC. Over the past two seasons, Nabers has 46 MTF on 152 Receptions. Nabers is also sure handed with just 9 Drops over his 215 Targets in this span. While age is never an extremely important factor, it is probably most important for younger players showcasing rapid improvement at young ages, of which Nabers fits the bill. Nabers has increased his Yards per Route Run from 1.95 (Age 18) to 2.46 (Age 19) to 3.79 (Age 20). The nearly 4 Y/RR figure as a player who’s conference is the SEC is quite impressive. While it is a one game sample, Nabers Y/RR was over 4 against Bama, as well as plenty of other solid teams this year.
[...]
While Nabers is ever-so-slightly putting himself in front of the pack (non-MHJ edition for all of this), I do feel that the next two after Nabers combine to make the three most well-rounded profiles in this class with Rome Odunze and Emeka Egbuka. They each also seem to have something that they do better than the others. Odunze is the best at the catch point, Egbuka is the best route runner / separator, and Nabers is by far the best with the ball in his hands. [...] He may not be Harrison, but Nabers is an extremely talented player who, at almost a full year younger than MHj, is in position to win the Biletnikoff after dominating SEC competition for over a full season.
—

1. Marvin Harrison Jr., WR, Ohio State (Age: 22.01)

Unofficial: 6’ 4”, 205 lbs
It almost physically pains me to put Marvin Harrison Jr. at #3. I considered re-arranging the tiers to reinforce the idea that these are in-fact tiers, but I do stick by the logic that in a 12-Team SF format, the needs at QB are generally too great to pass on QBs graded as highly as Williams & Maye. That said, due to the position alone, Harrison is by far the safest player in the first tier of prospects. I’m not sure if he is a full 6’ 4” or not, but Harrison is a tantalizing mix of size, speed, bully-ball catch ability, and stunning polish for a player with his size and physical traits. Simply put, players that size are not supposed to move that smoothly and with that much controlled, mechanical nuance in their movements. Whether nature or nurture, Harrison Jr. truly represents a prospect who is a 2.0, a version of someone else who is bigger, faster, and stronger.
[...] While I have not officially ranked this long, for me, there are only two other prospects in the last ten draft cycles to be close to the same grade as Marvin Harrison Jr.: Ja’Marr Chase & Amari Cooper.
Perhaps no game for any prospect at any position has been more impressive this year than the Penn State game, the one that dubbed Harrison “Maserati Marv,” found here. [...] Some of the contested catches or slight adjustments (2:34, 2:41) on this tape are just other-worldly. The play at 0:39 in particular is such an aggressive example of Harrison’s ability to make contested catches. This pass is not thrown to a good window, and Harrison essentially just tells the CB he isn’t allowed to have the football and bulldozes through him (and a DPI) for a solid gain. [...]
[...] Most simply put, you cannot go wrong drafting Marvin Harrison Jr.
—

1. Drake Maye, QB, North Carolina (Age: 22.00)

Unofficial: 6’ 4”, 230 lbs
Drake Maye is the prospect most defined by one number: 78 BTTs. In 2022, Drake Maye had 45 Big Time Throws (PFF), 10 more than anyone else in the FBS and 13 more than anyone else in the P5. So far in 2023, Drake Maye has 33 BTTs, 5 more than anyone else in the FBS/P5. PFF defines a Big Time Throw as “a pass with excellent ball location and timing, generally thrown further down the field and/or into a tighter window.” While this is just a subjective number, the high difficulty, downfield, and tight window throws on Maye’s tape are extremely impressive. [...]
While Maye is far from a perfect prospect, even the negatives for Maye I find to be at times overblown. For example, while there have been several individual plays on Maye’s tape that you can point to as being examples of poor, over-aggressive decision making, the ratio of these plays occurring is actually very low, particularly when you consider the tendency to throw the ball downfield. Maye has 9 Turnover-Worthy Plays, but 7 Interceptions. This suggests at least a bit of poor luck. Maye is tied for the 8th lowest TWP% in the FBS in 2023 among QBs with at least 250 Dropbacks (103 Total QBs). [...] It is hard to develop confidence in processing at the CFB level, and I’m not sure I have developed it with Maye, but it is encouraging that Maye went through a change at Offensive Coordinator this season and did not suffer for it. Williams, McCarthy, Daniels, and Penix Jr. in particular are all starting for their 2nd or 3rd year under the same offensive mind, so this is an advantage Maye did not have.
Maye has a few faults of course, but the high-end plays are some of the most obvious to see of any QB I have ever scouted. For this review, I’ll be using the Duke game. To begin on something a bit absurd, I have no idea what the translatable quality is here, but Maye has made a number of throws this year while battling contact, including at 0:00, 1:36, and the game-winner at 3:12. Earlier in the season, he even made a left handed TD pass found here. What is clearly translatable is his ability to throw perfect passes down the sideline (2:08) and into the End Zone (2:31). And at 0:45, we see Maye on the run to his left floating perfect, spectacular passes down the field. [...]
[...] I do rank Drake Maye more highly than any QB in the last two cycles, and higher than any QB but Trevor Lawrence in the last three.
—
1.. Caleb Williams, QB, USC (Age: 22.09)
Unofficial: 6’ 1”, 215 lbs
As we’ve gotten closer to the season, Caleb Williams has certainly gained some detractors. Ultimately, they might be right. Quarterback is one of the toughest positions to play, and one of the hardest to truly hit at. But we cannot forget that Caleb Williams is extremely talented. While Maye is 1st in Big Time Throws in each of the past two seasons, Williams was 2nd in 2022 and will likely be a few BTTs away from 2nd in 2023. The main trait that separates him in theory from Maye though is his rushing ability. [...] Williams has appeared in 34 Career Games (all FBS) since entering for Oklahoma against Texas in 2021. In that time, he’s 732 of 1,097 (66.7%) for 9,996 Yards, 93 TDs, and 14 INTs with 232 Carries for 1,539 Yards (6.6 YPC) and 27 TDs.
There is one area that I do understand the high level of criticism, and that is with fumbling and protecting the football in that capacity. His 14 INTs in 1,097 PAs is fantastic, but I believe Williams has put the ball on the turf a total of 33 times in his career. This is definitely something that you hope to slowly at least fade out of his game a bit at the next level. [...] All of that said, Williams’ numbers on deep passes go to show why he is still such a coveted player. In the last two seasons, on passes that have gone 20+ Yards downfield or further, the passes that Williams holds the ball for, Williams is 70 of 152 for 2,722 Yards (17.9 YPA), 21 TDs, 5 INTs, 50 Big Time Throws, and 8 Turnover Worthy Plays.
With Williams, the biggest challenge comes from watching all of these plays and trying to figure out what particularly will NOT work at the next level. To highlight some of these plays, I’ll be using the Washington game. Right away we see some “this probably won’t work in Williams’ first play shown in the video (0:07). The ending and the end result are superb including the ball placement, highlighting those downfield passing stats shown above, but it is a play that at the very least involves a lot of controlled chaos. Williams does move around quite a bit, but it is important to point out that he is not always moving backward (2:04). On top of that, for all the talk we hear about “QB Wins” involving Caleb Williams, Williams twice nails an incredible downfield pass for a game-tying TD late (2:16, 2:39). [...]
There has been a lot of discourse surrounding Caleb Williams, but QB scouting in particular is not necessarily about looking for perfection. [...] Right now, I expect Williams to be a top 2 NFL pick, a top 3 fantasy pick, and someone you should be overwhelmingly happy to draft at the top of a SF league. Caleb Williams may be a new breed of elite prospect that looks nothing like Stafford, Luck, or Lawrence (or even Maye), but he is an elite QB prospect.
//
That’s all I have for now. Previous parts can be found in the Comments below. For anyone who has followed my Risers series on College Football this year, there will likely at least be a Recap edition after Rivalry or Conference Championship week. Other than that, I will likely not be writing nearly as much as I did this week for quite some time. The next “Big Board” type piece will likely be early-to-mid February after declarations are finalized and when we have a bit more information. Perhaps I will find a few specific CFB topics to talk about though depending.
Feel free to leave any questions / comments below. I will get around to everything when I can.
Thanks all,
C.J.

data = formatting_prompts_func() trainer = SFTTrainer( model=model, train_dataset=data, # eval_dataset=dataset, peft_config=peft_config, dataset_text_field="text", max_seq_length=2600, # formatting_func=formatting_prompts_func, tokenizer=tokenizer, packing=True, args=training_arguments, ) 

with training arguments as

training_arguments = TrainingArguments( per_device_train_batch_size=2, gradient_accumulation_steps=2, optim="paged_adamw_8bit", logging_steps=1, learning_rate=1e-4, fp16=True, max_grad_norm=0.2, num_train_epochs=2, evaluation_strategy="steps", eval_steps=0.2, # max_steps=-1, save_strategy="epoch", #group_by_length=True, output_dir= "/content/", report_to="tensorboard", save_safetensors=True, lr_scheduler_type="cosine", seed=42, ) 

this the trainer im using With "meta-llama/Llama-2-7b-hf" but have custom data consist of json

{ "set1": { "Scenario": "baking a cake", "Steps": { "step1": { "The hint": "buy the necessary ingredients", "Choices": "0.Let cool1.remove from oven2.Mix cake according to instructions3.add the cake4.Go to stor", "The Choice made": "Mix cake according to instructions", "Point Acquired": "-1", "Total reward ": "-1", "Lives Left": "4", "Completed": "0.0" }, ... "step12": { "The hint": "wait until finished", "Choices": "0.Take out cake supplies1.Preheat oven according to box directions2.Bake in oven according to time on instructions.3.Purchase ingredient", "The Choice made": "Bake in oven according to time on instructions." } }, "Result": "GAME OVER YOU WON!!" }, "set2": { "Scenario": "baking a cake", "Steps": { "step1": { "The hint": "buy the necessary ingredients", "Choices": "0.Let cool1.remove from oven2.Mix cake according to instructions3.add the cake4.Go to stor", "The Choice made": "Mix cake according to instructions", "Point Acquired": "-1", "Total reward ": "-1", "Lives Left": "4", "Completed": "0.0" }, ... "step9": { "The hint": " make cake", "Choices": "0.take out and frost cake1.make the chocolate mixture2.Check if the cake is ready3.Turn off oven.4.Apply icing or glaz", "The Choice made": "Turn off oven.", "Point Acquired": "-1", "Total reward ": "-5", "Lives Left": "0", "Completed": "12.5" } }, "Result": "GAME OVER YOU LOST!!!" } } 

and provide the data to trainer as

def formatting_prompts_func(): abc = get_listdat() # reads and provides above listed json i = 1 frmmtedArr = [] while i <= len(abc): strall = "" # print(f"{strall} is strall") st = "set"+str(i) x = abc[st] i+=1 for ky, val in abc.items(): if ky == "Scenario": snval = "Scenario " + val if ky == "Steps": c = 1 while c<= len(val): stp = "step"+str(c) vals = val[stp] c+=1 hnt = " The hint " +vals.get('The hint') chcs = ' Choices '+vals.get('Choices') chsmde = ' The Choice made '+vals.get('The Choice made') try: rwrd = ' Reward '+vals.get("Point Acquired") except TypeError: pass print(f"{snval}{hnt},{chcs}{chsmde}{rwrd}") frmmtedArr.append(snval + hnt + chcs + rwrd) df = pd.DataFrame(frmmtedArr, columns=["text"]) dataset = datasets.Dataset.from_dict(df) return dataset 

when I excuse trainer.train() I get

IndexError Traceback (most recent call last)  in () -- 1 trainer.train() 2 trainer.save_model() 11 frames /uslocal/lib/python3.10/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs) 1589 hf_hub_utils.enable_progress_bars() 1590 else: -> 1591 return inner_training_loop( 1592 args=args, 1593 resume_from_checkpoint=resume_from_checkpoint, /uslocal/lib/python3.10/dist-packages/transformers/trainer.py in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1868 1869 step = -1 -> 1870 for step, inputs in enumerate(epoch_iterator): 1871 total_batched_samples += 1 1872 if rng_to_sync: /uslocal/lib/python3.10/dist-packages/accelerate/data_loader.py in __iter__(self) 558 self._stop_iteration = False 559 first_batch = None 560 next_batch, next_batch_info = self._fetch_batches(main_iterator) 561 batch_index = 0 562 while not stop_iteration: /uslocal/lib/python3.10/dist-packages/accelerate/data_loader.py in _fetch_batches(self, iterator) 521 batches = [] 522 for _ in range(self.state.num_processes): 523 batches.append(next(iterator)) 524 batch = concatenate(batches, dim=0) 525 # In both cases, we need to get the structure of the batch that we will broadcast on other /uslocal/lib/python3.10/dist-packages/torch/utils/data/dataloader.py in __next__(self) 628 # TODO(https://github.com/pytorch/pytorch/issues/76750) 629 self._reset() # type: ignore[call-arg] 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ /uslocal/lib/python3.10/dist-packages/torch/utils/data/dataloader.py in _next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) /uslocal/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 30 for _ in possibly_batched_index: 31 try: - 32 data.append(next(self.dataset_iter)) 33 except StopIteration: 34 self.ended = True /uslocal/lib/python3.10/dist-packages/trl/traineutils.py in __iter__(self) 572 more_examples = False 573 break 574 tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"] 575 all_token_ids = [] 576 for tokenized_input in tokenized_inputs: /uslocal/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, text_target, text_pair_target, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 2788 if not self._in_target_context_manager: 2789 self._switch_to_input_mode() -> 2790 encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs) 2791 if text_target is not None: 2792 self._switch_to_target_mode() /uslocal/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in _call_one(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 2874 ) 2875 batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text -> 2876 return self.batch_encode_plus( 2877 batch_text_or_text_pairs=batch_text_or_text_pairs, 2878 add_special_tokens=add_special_tokens, /uslocal/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in batch_encode_plus(self, batch_text_or_text_pairs, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 3065 ) 3066 -> 3067 return self._batch_encode_plus( 3068 batch_text_or_text_pairs=batch_text_or_text_pairs, 3069 add_special_tokens=add_special_tokens, /uslocal/lib/python3.10/dist-packages/transformers/tokenization_utils_fast.py in _batch_encode_plus(self, batch_text_or_text_pairs, add_special_tokens, padding_strategy, truncation_strategy, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose) 535 # we add an overflow_to_sample_mapping array (see below) 536 sanitized_tokens = {} 537 for key in tokens_and_encodings[0][0].keys(): 538 stack = [e for item, _ in tokens_and_encodings for e in item[key]] 539 sanitized_tokens[key] = stack IndexError: list index out of range 

can anybody tell me what Im doing wrong

Hi everyone,
I'm currently finishing the last project of the Responsive Web Design Course on freeCodeCamp, which basically requires me to build a fake portfolio website.
I wanted to take this last project as an opportunity to push myself a bit further than what freeCodeCamp asks me to realize (here their mockup sample).
Since I was tired of all those basic and boring regular shapes and plain border-radius solutions, I wanted to dive into SVG shapes to give my fake portfolio a more vibrant look and learn something new (the attached image showing the painted shoulder inspired me for colors and shapes btw).
MY GOAL : I wanted to create the layout I sketched in my notebook (see attached photo) using SVG backgrounds and by creating several petal-shapes SVG to contain images that would get the same shape of the SVG petals. In order to create that custom and complex SVG petal shape I used this free online tool (https://fffuel.co/ssshape/), which created a shape with the following HTML code that I embedded in my code:

MY PROBLEM: I read several articles explaining how to create simple SVG shapes on my own in HTML and fill them with images, and so far I think I'm ok. But when it comes to more complex shapes generated with online tools I can't really figure out how to edit them properly in order to replace that monocrome background (hsl(340, 45%, 50%)) with an image (see the attached screenshot showing the current state of my webpage). I tried different approaches but none of them worked out and I can't really figure out a solution. So yeah, I'm really stuck here guys.
I mean, is it even possible doing what I want to achieve with these kind of SVG shapes by using only HTML+CSS and without using more advanced tools like Photoshop etc.?
Btw, I still have to learn JavaScript, so don't count on me for that kind of solution (yet).
Also, I'm realizing that working with SVGs involves a lot of 'position: absolute' elements, which isn't great for responsiveness. But hey, one problem at a time.
Anyway, I just hope someone here can get me some hint to help me figuring out a solution.
I know I'm not giving you much context about my HTML code, but I didn't want to make this post become the longest text wall on Reddit :)
Thank you!
​
EDIT: I added the images I was referring to cause I realized they weren't visible in the first place.
​
​
The photo that inspired my design
​
The photo of my mockup sketch
​
The current (and messy) state of my fake portfolio

Intro

When I was 7 years old, I spent my summer tenaciously trying and failing to defeat a behemoth: my older brother and his bullshit Machamp. I didn’t understand it; I grinded for hours, training on Mt. Silver and cycling through the Elite 4 again and again to get my Pokémon leveled up and strong as possible. But regardless of how strong my Pokémon were, my brother would just grind me down until I had to put up my level 100 Tyranitar against his stupid, under-leveled Machamp, who would then proceed to tank a Rock Slide and then KO my prize fighter with a Cross Chop. My problem, you see, was that I was an idiot. All I cared about was getting the highest base-stat Pokémon I could find and training them to maximize those stats. I didn’t give a shit about type matchups or team synergy (in addition to my Tyranitar, I also always had a Typhlosion, Ho-oh, and Entei in my party), and focused solely on what my data told me were my best fighters.
I, like many of us, think about basketball in a similar way. Actually understanding basketball is hard. It’s complex, and fluid, and so many winning plays don’t show up in the box score. But numbers are easy. I just love looking at a spreadsheet that tells me which basketball player is the best in the league, and which players are complete trash. And the best part: I can pretend like my awful opinions are smart and data-driven while those “eye-test” morons are nitpicking and biased – I win, bye bye!
Now this got me thinking – what if I could combine 3 of my favorite things: NBA basketball, Pokémon, and needless, contrived data analysis? After much thought, I decided I wanted to create a definitive Rosetta Stone so that basketball nerds could efficiently communicate with Pokémon nerds. And after months of development, testing, and tweaking, I am happy to introduce my official NBA Player to Pokémon Crosswalk.

Methods

I first must address that this crosswalk isn’t comprehensive. There are way too many Pokémon to compare to current NBA players who played meaningful minutes, and way too many former and current NBA players to compare to Pokémon. I thusly limited my dictionary to players who played at least 1,100 minutes in the 2022-2023 NBA regular season and Pokémon from the first 2 generations. There are a few reasons for this. A few of the statistics I use are pretty noisy and need a good amount of playing time to stabilize, and I wanted to limit this analysis to notable players who made an impact this past year. On the Pokémon side, I have the strongest memories and fondness for these first two generations of games and didn’t want to include a bunch of Pokémon that I didn’t really care about. But the real reason for these choices is simple: There are 251 Pokémon in the first 2 generations and 251 players who played at least 1,100 minutes. And with this perfect symmetry, I can ensure that every player has a unique Pokémon and every Pokémon has a unique player.
As I said earlier, I don’t care about type matchups or move sets or positions or player fit. I only care about numbers and base stats. So I developed the following basketball analogues for each Pokémon base stat:
HP:
(Career games played)/(Career possible games played)
Pretty simple: How many games did the player play in for their entire career vs how many they could have played in. I like this because it incorporates games missed due to injury and games missed due to being bad. This is the only stat that uses career statistics because I thought that single injuries to players who are normally consistently on the court could unfairly impact their ratings here. I also applied Bayesian normalization to bring their rates closer toward the league average, with players who have played for longer being less affected by the normalization than rookies.
Attack:
PPG*0.75+TS%*0.25
Attack is all about getting buckets. I rescaled both PPG and TS% to 0-100 and created a weighted average of the two rescaled statistics. I think both volume and efficiency are important, but I definitely value volume scoring on decent efficiency more than scoring 6 PPG on 75% TS.
Sp. Attack
Assists-Turnovers
I thought of special attack as playmaking. I initially used assist/turnover ratio, but that is really biased towards bigs who don’t touch the ball much – according to ATR, Kevon Looney is the 4th best playmaker in the league. It’s important to have the ball in hand to be a good playmaker, so I like just having this linear penalty for turnovers.
Defense
Raptor box defense
This is where things start to get weird. Defense is a really hard attribute to quantify for a multitude of reasons, so I didn’t really know where to go. So I let the eggheads over at FiveThirtyEight do that for me. RAPTOR box defense is an all-in-one defensive metric similar to defensive BPM that isn’t great, but none of the all-in-one metrics are. This is the best I could do.
Sp. Defense
(Raptor on/off defense)-(Raptor box defense)
This is where things get REALLY weird. RAPTOR uses a blend of box score statistics and on/off statistics for their overall score, and I just took the difference between the on/off score (representing the actual overall defensive impact of the player) and the box score (representing the predicted value of the player’s defense). My idea was that this difference would be all the intangibles that go into defense – switching, team defense, versatility, etc. I don’t know what “special” defense in basketball should be, but that kind of sounds like it makes sense to me.
Speed
(NBA.com average speed)*0.5+(2k23 Speed rating)*0.5
Of course, I had to site the utmost authority on player ability: NBA 2K. I could have made the entire stat just be the 2k speed rating and that decision would have been unassailable. But because I am a sucker for advanced analytics I made it a 50/50 blend with the average speed from NBA.com’s player tracking data. I don’t know how well this stat correlates to actual top sprint speed (Is Sam Hauser faster than De’Aaron Fox? I don’t know – I’ve never watched basketball before), but I would be remiss to not include a statistic that shows how fast each player is, on average, when they are playing basketball.
I then ranked every player and every Pokémon by each stat and gave each NBA player the corresponding Pokémon base stat. So, for example, the player with the highest attack stat would be given Tyranitar’s (or Dragonite’s) 134 attack and the player with the lowest attack would be given Chansey’s 5. Once I Pokémonified each NBA player’s stats I was able to 1) sum up each stat to get their NBA base stat total and 2) match each NBA player to their corresponding Pokémon.
The matching algorithm was pretty simple. I compared all six stats for each of the 63,001 combinations of players and Pokémon and calculated the Mean Square Error (MSE), which is just the average of the squared difference between Pokémon and player for each stat (e.g. the squared difference of Porygon’s HP and Kelly Olynyk’s HP plus the squared difference of Porygon’s Speed and Kelly Olynyk’s Speed, etc). I then sorted all 63,001 combinations from lowest MSE (best match) to highest MSE (worst match) and picked the first match that didn’t have either the Pokémon or the player picked already. This was to get the best match for each individual without having any repeat players or Pokémon. And then I had it: the definitive, objective list of every meaningful NBA player and their Pokémon counterpart.

Results

The distribution of the total base stats was interesting . Because I used the exact same 251 numbers from each Pokémon to give to each NBA player, the NBA players and Pokémon had the same distribution for each individual stat. But, although the mean total base stats were the same (406.7, approximately a Charmeleon), the distribution of Pokémon total stats and player total stats looked a lot different. This figure is a density plot of the total base stats for NBA players (in red) and Pokémon (in yellow). The players have a very central distribution with a mode very near the mean at around 400, but the Pokémon have a bimodal distribution; there are a lot of Pokémon with base stats of around 500 and a lot of Pokémon with base stats around 300, with fewer hovering around that mean score. Both have a tail on the right side where there are a few individuals who are way more powerful than the rest.
I also looked at stats by position. This set of bar graphs shows the mean stats for each position according to basketball-reference. A few notable findings:

• Overall stats showed a slight monotonic decrease from PG to C (p<0.0001)
• Special attack, as expected, was drastically higher among PGs and pathetically low for C.
• Conversely, Cs had a much higher average defense stat compared to the other positions. Although they also had the lowest average special defense.
• Centers are really slow, but I was surprised to see SGs edging out PGs slightly in the speed category.

To assess the correlation between top-tier players and top-tier Pokémon, I used the competitive viability rankings for Pokémon Gold/SilveCrystal. I compared players receiving an all-NBA selection to Pokémon that were at least in the B-tier of competitive viability, including the 5 Uber-tier Pokémon. I’m not into competitive Pokémon because I’m not a freak but I figure this list is approximately as good at picking the best Pokémon as the all-NBA voters are at picking the best players in the league. And indeed, there was a strong association here, albeit on a small sample size: 5 out of 15 all-NBA selections (33.3%) were matched to a competitively viable Pokémon, compared to only 18 out of 236 players who did not make an all-NBA team (7.6%).
But I just thought that these data were kind of cool. Let’s get into the really interesting results. Here are the top 10 worst NBA players by Pokémon base stats:
10: Seth Curry
Total: 293 – HP: 35 – ATK: 57 – SP.ATK: 58 – DEF:40 – SP.DEF: 55 – SPD: 48 – Match: Dratini
I was devastated when I first saw this and misread it as analytics darling Steph being the 10th least powerful player in the NBA until I reread and saw it was his bum-ass brother. He’s pretty garbage at everything here, especially staying on the court.
T9: Precious Achiuwa
Total: 292 – HP: 60 – ATK: 47 – SP.ATK: 25 – DEF:75 – SP.DEF: 25 – SPD: 60 – Match: Nidoran (F)
I think Achiuwa is going to be a powerful player one day. He’s big and strong and has shown flashes of real impressive talent. But his injury definitely hurt his stats (he barely played enough minutes to make the list) and the man has to start passing more if he wants anything close to a decent SP.ATK.
T9: P.J. Tucker
Total: 292 – HP: 100 – ATK: 5 – SP.ATK: 36 – DEF:70 – SP.DEF: 40 – SPD: 41 – Match: Marill
I have to admit, I’m pretty happy he wound up on this list. P.J. has really pissed me off for years. I just feel like every playoffs he has like 2 games where he goes 12/13 from 3 in 15 minutes of play and just completely fucks my team. But I’m glad to see that most of the time he just stands in the corner and does nothing, giving him Chansey’s impotent 5 ATK, which is so bad that it brings him down to the bottom 10 despite some decent HP and defense stats.
7: Trendon Watford
Total: 290 – HP: 40 – ATK: 50 – SP.ATK: 60 – DEF:55 – SP.DEF: 45 – SPD: 40 – Match: Ditto
I know absolutely nothing about Trendon Watford. I don’t even think I’ve heard of him. He’s just pretty bad at everything without being atrocious, he played on the unremarkable Blazers, and he doesn’t have any interesting highlights I can find. Pretty cool that his match is Ditto, though. I like to imagine he can provide value to Brooklyn by morphing into Mikal Bridges and getting traded to the Timberwolves for Gobert and picks or something, only to morph back into an amorphous blob upon his arrival in Minnesota.
T6: David Roddy
Total: 280 – HP: 60 – ATK: 20 – SP.ATK: 30 – DEF:58 – SP.DEF: 40 – SPD: 72 – Match: Smeargle
I’ll withhold judgement for now as he’s only played one year, but he’s got absolutely nothing on offense right now. Also, he looks like the final boss of football players at the YMCA. Would be an absolute nightmare to guard in pickup.
T6: Lamar Stevens
Total: 280 – HP: 45 – ATK: 10 – SP.ATK: 35 – DEF:90 – SP.DEF: 35 – SPD: 65 – Match: Magikarp
Lamar Brandon Stevens (born July 9, 1997) is an American professional basketball player for the Cleveland Cavaliers of the National Basketball Association (NBA). He played college basketball for the Penn State Nittany Lions.
Another guy I know nothing about, so I just copied from his Wikipedia page.
4: Reggie Bullock
Total: 274 – HP: 39 – ATK: 40 – SP.ATK: 60 – DEF:35 – SP.DEF: 65 – SPD: 35 – Match: Mareep
I’m pretty surprised how far down he is. I always thought of him as a solid role player who would be somewhere in the mid-low tier of players. And I do think he was done a bit dirty here. He’s been pretty reliable the past few years, but still has an abysmal HP stat due to being unable to stay on the court in his early years. But on the other hand, dude can not shoot at all.
3: Bol Bol
Total: 270 – HP: 20 – ATK: 60 – SP.ATK: 15 – DEF:40 – SP.DEF: 85 – SPD: 50 – Match: Rattata
I remember back when he was still in high school, I watched a video where this guy said that one day Bol Bol would dunk from the three point line. I spent years believing this random dude before I finally realized that was incredibly stupid and I was incredibly stupid for thinking that was true. I still was super bullish on him though. His draft day NBA comparison was Kristaps Porzingis. Now his Pokémon comparison is Rattata.
2: Christian Wood
Total: 265 – HP: 25 – ATK: 90 – SP.ATK: 35 – DEF:60 – SP.DEF: 35 – SPD: 20 – Match: Paras
Hopefully this will once and for all end the Christian Wood discussion. He’s been a vexing player for his whole career, bouncing around from team to team and never quite playing up to his potential. Now being on the Lakers, he’s got a bit of buzz again. But I don’t know if he’ll ever be able to shake his new reputation as one of the least powerful players in the league, nor his new comp: a weird nerdy little mushroom crab.
1: Jordan Nwora
Total: 256 – HP: 40 – ATK: 45 – SP.ATK: 40 – DEF:20 – SP.DEF: 55 – SPD: 56 – Match: Zubat
This one hurts. I like Jordan Nwora. I have family from Louisville and I remember watching him play. I really want to emphasize that by even making the list that means he was been an impactful player in the NBA this year. But to not only be the weakest measured player in the league, but to also have the most hated, annoying, useless fucking Pokémon in history be your comparison? Just brutal, man.
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On the flip side, here are our top 10 most powerful players:
10: T.J. McConnell
Total: 535 – HP: 90 – ATK: 50 – SP.ATK: 100 – DEF: 95 – SP.DEF: 70 – SPD: 130 – Match: Starmie
The quintessential high-motor, scrappy gym rat kind of player. Real lunch pail guy. He may not be in many top 10 NBA players lists, but McConnell sneaks into this one by the sheer force of trying really, really hard. He actually had the highest average game speed in the entire NBA last year, which gave him a blistering 130 speed stat despite a pedestrian 76 speed in 2k.
9: Tyrese Haliburton
Total: 539 – HP: 65 – ATK: 100 – SP.ATK: 154 – DEF: 45 – SP.DEF: 80 – SPD: 95 – Match: Espeon
Haliburton tops the league in special attack, and deservedly so. Certified point god. I am more interested in how fucking good Espeon is, however. I, being a young boy at the peak of my Pokemon fandom, naturally figured Espeon was for girls so I never even considered it for my party. But its special attack and speed are no joke. I could’ve done some damage against my friends if it weren’t for my deep-seated misogyny.
T8: DeMar DeRozan
Total: 545 – HP: 140 – ATK: 105 – SP.ATK: 95 – DEF: 65 – SP.DEF: 100 – SPD: 40 – Match: Lapras
DeMar is so cool. I’m disappointed that his Pokémon comp is Lapras. I mean, Lapras is also cool for sure, but definitely not in the upper echelon of sick Pokémon like Charizard or Gyarados, which I think would be better vibe matches with DeRozan. Side note: I had no idea how reliable DeRozan has been throughout his career. He’s played in at least 70 games in 10 of his 14 years in the NBA. Super cool.
T8: Giannis Antetokounmpo
Total: 545 – HP: 105 – ATK: 130 – SP.ATK: 80 – DEF: 100 – SP.DEF: 65 – SPD: 65 – Match: Machamp
Machamp is the perfect comparison, IMO. Like Machamp, Giannis is objectively awesome, cool, and likable. Also like Machamp, I fucking hate him. And I hate them both for the same reason: they just destroy everything I care about with overwhelming power. I wouldn’t be surprised if Giannis entered next season with two extra arms that he grew this summer and scored 600 PPG.
6: Ja Morant
Total: 555 – HP: 65 – ATK: 110 – SP.ATK: 115 – DEF: 75 – SP.DEF: 100 – SPD: 90 – Match: Magmar
“Ja Morant used Fire Blast! It’s super effective! Foe 17-Year-Old Boy fainted. Ja received a 4 game suspension for winning!”
5: Franz Wagner
Total: 589 – HP: 100 – ATK: 90 – SP.ATK: 70 – DEF: 78 – SP.DEF: 154 – SPD: 97 – Match: Articuno
This is what I was talking about with DeMar. If Wagner and DeRozan switched Pokémon comps, the vibes would match so much better. Deebo gets matched with one of the coolest Pokémon out there and Wagner gets matched with a long-necked enigma that I don’t understand. Why are his defensive on/off splits so good? Is he actually a good defender? I don’t think I’ve ever watched a Magic game in my life, so I have no idea.
4: Nikola Jokic
Total: 595 – HP: 130 – ATK: 125 – SP.ATK: 130 – DEF: 160 – SP.DEF: 25 – SPD: 25 – Match: Mewtwo
No surprises here seeing Jokic taking one of the top spots. And his stats are so much cooler than everyone else’s. Absolutely terrifying HP, attack, special attack, and defense, with pitiful special defense and speed. But who cares about those nerd stats? He’s just pure Chad.
3: Mikal Bridges
Total: 630 – HP: 250 – ATK: 95 – SP.ATK: 80 – DEF: 70 – SP.DEF: 50 – SPD: 85 – Match: Blissey
If there’s any identifiable problem with my algorithm, it’s the extreme outlier stats. Since I match the ranks of player and Pokémon stats, Pokémon like Chansey, Blissey, and Shuckle can really inflate the base stats of the top performers in certain categories. Bridges is the first of two ironmen who, despite being an excellent player, is maybe not better than Jokic and Giannis.
2: Buddy Hield
Total: 635 – HP: 255 – ATK: 85 – SP.ATK: 65 – DEF: 60 – SP.DEF: 70 – SPD: 100 – Match: Chansey
The second ironman, Buddy Hield narrowly takes the two spot over Bridges, thanks to his superior speed. My consolation to these two being so high on the rankings is that at least they both have lame comparisons. Like cool, Buddy, you’re a top 2 player in the league. You’re also a big egg holding a little egg in your pouch.
1: Immanuel Quickley
Total: 669 – HP: 105 – ATK: 80 – SP.ATK: 85 – DEF: 79 – SP.DEF: 230 – SPD: 90 – Match: Lugia
As we all expected, Immanuel Quickley takes the top spot. After dominating the league for years with his well-balanced attack and Shuckle-like special defense, Quickley finally earns a worthy comparison: the sickest legendary Pokémon in the whole series. Maybe it’s a little too chalky, but this is great validation that my algorithm, while perhaps not perfect, is getting the broad strokes correct.

Discussion

There is so much more to this dataset to look at and discuss (For instance: Luka is matched to GOAT Tyranitar and Chris Paul is matched to Sunflora -- ha), but for the sake of brevity I’ll save that for another time. Here is the link to the spreadsheet with all the stats for every Pokémon and their NBA match so if anyone else wants to explore how strong their favorite players are or which player embodies their favorite Pokémon they can.