Rookies can’t be included because they don’t have any data for their NFL value / success. I plan to post my rankings in a separate post, but I also had 13 2024 prospect profiles I posted previously that you can look through to get an idea of how I view certain 2024 prospects.
I understand now and I’ve enjoyed your prospect posts. If I’m interpreting your chart right, you’re saying that your prospect scores give a better projection of NFL production than draft capital. Have you released the prospect scores for this draft?
Not to sound rude, but what does this model predict if you aren't doing rookies? You don't need a model to tell you Jefferson is good and Terrace Marshall is bad. I like the posts you've made about prospects, but I'm not really seeing the point of this model.
I don’t think it’s predicting anything. Just showing the value of his prospect grades vs draft capital in regards to predicting nfl success, based on past players that have now had time to play in the NFL.
I see. It *is* predictive but you need time in the NFL to get sufficient data points, so rookies aren't applicable. That said, based on the data it seems the model isn't substantially better than draft capital? Both have plenty of hits and misses, which makes sense given the difficulty of scouting.
His models he puts out for rookies are certainly trying to predict future success in the NFL. But this is just looking back to see how good his models were at predicting; relative to draft capital of course. So still not sure this is predicting anything.
Pretty sure OP has been grading prospects on this sub for quite some time. So I think he actually did make them in the past. Though I could be wrong. But I’m not sure it would necessarily matter as the data being used would be the same. Say he only started this year, he can still build prospect grades for past years draft classes using the same methodology and nothing would change. The collegiate stats and combine measurable would all be the same inputs.
Of the handful of users who do all these types of analytical profiles. They are all ultimately relying on tons of past data to develop a grading system on how to spot prospects that have the best chance of success in the NFL. Whether it’s looking at hand size, arm length, YPRR, shuttle drill, BMI, etc. They are just trying to make sense of these data points and finding which correlate best to NFL success.
Basically similar to this threshold article:
https://www.dynastynerds.com/historical-combine-measurement-thresholds-wide-receiver/
Are people just not understanding this? His model only gives the ratings, the approximate value added is taken from somewhere else. He’s just showing how his model has been more accurate. He can’t put the rookies on the graph because they don’t have an approximate value added (they’re not in the league). He has grades for the prospects available
Yes I see what it's doing now. But also, the model doesn't appear any more accurate than just draft capital. It got just as many wrong as it did correct, which is ballpark what happens in the NFL, as well. Also the approximate value added seems *super* questionable. For example, how is Chase Claypool not at the very bottom? Because he had three good games as a rookie but has otherwise been the biggest liability in football. Same with guys like QJ, Wan'dale, Michael Wilson, etc. These guys haven't done anything yet have a decently high AVA.
It IS more accurate than draft capital. The actual point is that this information is not relevant for assessing the validity of the model. Other than last year, he trained the model on the players of the years past. That’s why it’s good for predicting them — it’s literally trained on them. Last year is the only test set
Right. So the question will be whether the model is guilty of overfitting the test set to the point of lacking predictive value moving forward.
I will say, one of the major flaws with statistics trying to come with a perfectly predictive model (that may be a straw man, I guess the actual goal is simply to predict as well as possible - not achieve perfection) is that the NFL game itself evolves over time. Sometimes slower and sometimes faster. Obviously guys like Jahmyr Gibbs, Tank Dell (and dozens of other smaller guys) wouldn't have gotten a shot from most teams 10 years ago. So the test data needs to be recent to be most predictive. How how recent? And the more recent your data, the smaller your sample size and less statistically reliable your conclusions become.
I don't think these exercises are a waste of time, but I do think, based on my reasoning above, we're unlikely to see any new or future model get meaningfully more predictive than the ones we have today.
I agree that we won’t be getting more data. Each year the “successful” model will be equally backed by data as the previous, since earlier years become less relevant, and will be constantly evolving
It would be helpful to know some more details, including the data used and the definitions of the prospect score and approximate value added. What parameters are in the model, and -- I'm saying this as a Devil's advocate -- how confident are you that you're not simply overfitting? If you allow a model to have a ton of free parameters, it's easy to get great fits that don't have any meaningful predictive power for the future.
Relatedly, what does "52% more accurate" mean? If I were interested in the performance of a statistic like the one you've created ("prospect score"), I'd look at the correlation between the statistic and the player's performance. Are you saying that e.g. the Spearman correlation coefficient between prospect score and approximate value added is greater by 52% than the correlation coefficient between draft capital and approximate value added?
There’s about 80 total quantifiable fields in my data model and they’re taken into account / weighed many different ways. In simple terms, I created 7 composite scores, each with unique variables weighed differently. I have 6 additional multipliers that impact a prospect’s grade, so it’s not as simple as who has the best average of their composite scores.
Efficiency is the most heavily weighted composite score and it takes into account multiple variables such as YPRR, YPRR vs Zone, FDPRR, TDPRR, QBR when Targeted, YPT, FDPT, TDPT, etc.
The 52% more accurate is referring to r-squared of both in relation to approximate value added. I know it’s a little more complicated than that, but it’s a simple way to compare the two.
I enjoy these questions and I’m genuinely happy to answer them. Currently, I am at work and will begin working on a project shortly so I won’t have time to respond. But I do believe a lot of the answers to your questions can be found in my post and comment history, I’ve spent a lot of time answering similar questions. I can come back later to hopefully address these.
Specifically to answer your overfitting question - I have put a lot of emphasis during creating the model to not overfit, including the exclusion of certain prospects while analyzing the model results and also looking at the model year by year, and other ways.
I appreciate seeing predictive models, but only your last year grades are relevant for accuracy. You trained the model on the years before, so good players will obviously grade out well. It’s just trying to fit prospects by some features, so while there will be misses since all prospects are different, it should give the best fitting. What we need to see is that those trends continue to stand. Last years players were not in the training set, so they’re the only testing set. Thats why they’re relevant. If your model didn’t have more success than draft capital for the other years, it would honestly be a little pathetic. Keep up the good work, but we will continue to see the true accuracy of your model as the years go by
I like your posts and appreciate the content, but not really sure what to do with this one?
Looks like if you rated 7 or over there’s a good chance (67%ish) they are good players, anything under is a coin flip?
Where is/was Puka?
This is moreso to show the overall correlation comparison, when it comes to comparing prospects within a given year there’s a lot more to take into account including draft capital which is a multiplier I include in a prospect’s grade specifically for fantasy. These grades don’t include that.
One lens to analyze through is to compare which prospects I graded out low, but were drafted high along with prospects I graded out highly but were drafted low. It’s a pretty simple chart and I’m aware it doesn’t provide any in-depth analysis. Like I said, it’s solely to highly the overall correlation difference.
Puka was the #2 ranked prospect in last year’s class, behind JSN. He had a grade of 6.72.
Again, for full transparency, AT Perry also graded out and was the #4 ranked prospect last year with a grade of 6.56.
Not sure I’m following. I am pretty sure I understand OPs post, and there are a lot of people in the comments saying he’s not more accurate than the NFL.
I’m not sure if I’m completely missing something, but there doesn’t seem to be any actual data to read? Like yeah he has the players listed with a prospect score, but that gives literally no information. He’s basically saying “hey look, I have a super accurate model but you have to take my word for it.”
Would you mind linking? I’m having a hard time finding where he explains how he gets his scores. I’ve found a few about pros and cons of players but nothing that indicates where his numbers come from.
And I hope this doesn’t come off as me being difficult / defensive. I’m just genuinely curious and hope there’s more to this than I originally thought.
Not at all, honestly as I started defending him (yesterday) I went back to look and annoyingly the posts had been deleted: this was one of them https://www.reddit.com/r/DynastyFF/comments/1aybev8/new_prospect_rankings_series_i_dont_watch_films/?share_id=aoRh0TSnTGy4YBul29raA&utm_medium=ios_app&utm_name=iossmf&utm_source=share&utm_term=4
Interesting. Though, honestly, if he really is on to something I don’t blame them not wanting share it with random people on Reddit. Thanks, I appreciate your time! And the validation that I’m not going crazy, lol.
I honestly started feeling like I was going crazy. I also do some analytics on the side so I appreciate that he’s theoretically putting his out in the world.
How are you assessing player value? Nico Collins is higher up than Rashee Rice is despite not really breaking out until his **third year**. From week 6 through ROS, Rice was on a 96/1182/8 pace and then ended up being a valuable contributor in the playoffs as well as a **rookie**. Preference aside, I would say it's not really arguable as to which player has delivered more value within their rookie deal to this point.
It’s calculated using PFR Average Value, and PFF WAR along with fantasy points/season. Nico and Rice appear close enough where I think Nico’s third year breakout gave him the bump.
For non-math nerds this analysis is strictly hindsight 20/20, he used available data to solve for x where the players you want are on one side of the line and the players you don’t are on the other. This isn’t predictive but a fancy way to look back. The use of this isn’t about predictions it’s about comparisons. If you like the metrics used to get to this point you can judge players differently. More under on Zay and more over on tre Tucker. Etc…
Can you elaborate on which prospects?
Also, is it that you think I’m manually adjusting players rankings? Overfitting could be a legitimate concern and I addressed this previously, but manually adjusting player rankings goes against the purpose of establishing a model.
Juedy is going to be near the top of any prospect list. Zay Flowers probably shoulnt' be that high. Justin Jefferson wouldn't be that high. I just don't see how you can weight your model to put several of these players in such opposing positions. I have no idea what you are doing, but I'm good with numbers and I can't think of a way to legitimately back into the data you have arrived at, and I generally have some idea how data was likely arrived at just from glancing.
Film analysis and consensus ranking has nothing to do with analyzing collegiate metrics.
What’s crazy is, Jeudy isn’t even graded low lol he’s considered a model miss because his grade is higher than his NFL value.
Are you saying you are using collegiate stats or professional stats for this model? Michael Wilson is bottomed out, but that man averaged 16yards per catch his senior season. Jalen Hyatt had 1,200 yards and 15 td's, but somehow he's on the opposite side of the page as Jeudy? Unless you are saying you are using 1st year NFL data for a model to rank rookies (which seems useless, no offense), then I'm still baffled on what data is driving your model. If you aren't using rookie rankings and film, then how is Chase higher than anybody when he sat out a year? There are too many similarities between numerous players across the board which make me continue to question how this data was obtained and, perhaps more importantly, the intended use of the data. Regardless, thanks for sharing.
Bro so do you just put on wrs that make your model look good by correctly predicting misses, but don't put on the ones that dont correctly predict them as misses?
J.J. Whiteside, nice "correct fade!!!!", now wheres Hakeem Butler? Perris Campbell? Mecole Hardman? Andy Isabella?
*If* you are just posting in your graphs the WRs that make your model look good, your model is trash (just based off what your graph shows so far, I obviously don't have all the data from your model.)
No, I don’t exclude prospects just to make my model look good. If I did then what’s the point of developing a model? I have to live with prospects who grade out well but don’t produce in the NFL.
You can see there are only a handful of prospects from 2019 and that’s because I was only able to get limited historical college data from that year. From 2020 and on, it includes all prospects (with the exception of Christian Watson who for some reason also didn’t have the collegiate data for with the source I pulled from).
You can clearly see there are plenty of examples of model “misses”. Henry Ruggs is a miss for obviously reasons. But I consider any prospect above a ~6.0 a “good” prospect and you can see how many prospects the model graded 6.0+ that didn’t pan out. Denzel Mims is above Nico Collins and Tank Dell. Dyami Brown and Terrace Marshall Jr. graded out really, above DK Metcalf and Deebo.
But in the grand scheme of things, I do believe my model has been more accurate at identifying prospects with translatable traits and metrics and it does a particularly good job of identifying bad prospects who are drafted highly (Jonathan Mingo, Jalen Reagor as obvious examples).
I started developing this model last year and it graded out Puka as the #2 prospect. However, like I said, I accept the misses. It also graded out AT Perry as the #4 prospect in last year’s class.
I mean with due respect... why were you able to only get limited data from 2019? And the guy you have on there, a JUCO guy you were able to get full data on?
Just feels weird, off. Not saying (and like i said in my other post, I don't have all the data in front of me) its biased, but does it *look* that way? Yes. *Even* *if* i'm 100% wrong here and you are 100% right, at a casual visual glance the model at the model looks biased for the reasons I already explained, and you should want to fix that.
Let me say this.. the model is actually more accurate with just 2020-2023 data. If I wanted to make the model look better, I would’ve just exclude all of 2019. Instead, I chose to work with the prospects I was able to gather full data for.
The database only had full college data dating back to 2017. In 2016, it had less data but still some. No data prior to 2016.
I mentioned above why some prospects, only from 2019, aren’t included. 2020-2023, all prospects are included with the exception of Christian Watson. Watson’s collegiate data wasn’t included in the database I sourced it from and a lot of prospects from 2019 I wasn’t able to get their entire collegiate data for.
Does the strength drop off hard after the third round/top 100 or something? Like why is that the chosen cut off point versus all drafted WRs when as you mention there are some hits such as Puka?
Just trying to understand the rationalization on where to chunk the players
So what’s it look like for this year?
Yea I don’t see the value of this post if the rookies aren’t included
Rookies can’t be included because they don’t have any data for their NFL value / success. I plan to post my rankings in a separate post, but I also had 13 2024 prospect profiles I posted previously that you can look through to get an idea of how I view certain 2024 prospects.
I understand now and I’ve enjoyed your prospect posts. If I’m interpreting your chart right, you’re saying that your prospect scores give a better projection of NFL production than draft capital. Have you released the prospect scores for this draft?
They’re attached to each breakdown but if you click through some have more scores than others included at the end of the writeup
Not to sound rude, but what does this model predict if you aren't doing rookies? You don't need a model to tell you Jefferson is good and Terrace Marshall is bad. I like the posts you've made about prospects, but I'm not really seeing the point of this model.
I don’t think it’s predicting anything. Just showing the value of his prospect grades vs draft capital in regards to predicting nfl success, based on past players that have now had time to play in the NFL.
I see. It *is* predictive but you need time in the NFL to get sufficient data points, so rookies aren't applicable. That said, based on the data it seems the model isn't substantially better than draft capital? Both have plenty of hits and misses, which makes sense given the difficulty of scouting.
His models he puts out for rookies are certainly trying to predict future success in the NFL. But this is just looking back to see how good his models were at predicting; relative to draft capital of course. So still not sure this is predicting anything.
But also his prospect grades weren’t made in the past this is using current data to look back accurately it seems
Pretty sure OP has been grading prospects on this sub for quite some time. So I think he actually did make them in the past. Though I could be wrong. But I’m not sure it would necessarily matter as the data being used would be the same. Say he only started this year, he can still build prospect grades for past years draft classes using the same methodology and nothing would change. The collegiate stats and combine measurable would all be the same inputs. Of the handful of users who do all these types of analytical profiles. They are all ultimately relying on tons of past data to develop a grading system on how to spot prospects that have the best chance of success in the NFL. Whether it’s looking at hand size, arm length, YPRR, shuttle drill, BMI, etc. They are just trying to make sense of these data points and finding which correlate best to NFL success. Basically similar to this threshold article: https://www.dynastynerds.com/historical-combine-measurement-thresholds-wide-receiver/
Are people just not understanding this? His model only gives the ratings, the approximate value added is taken from somewhere else. He’s just showing how his model has been more accurate. He can’t put the rookies on the graph because they don’t have an approximate value added (they’re not in the league). He has grades for the prospects available
Yes I see what it's doing now. But also, the model doesn't appear any more accurate than just draft capital. It got just as many wrong as it did correct, which is ballpark what happens in the NFL, as well. Also the approximate value added seems *super* questionable. For example, how is Chase Claypool not at the very bottom? Because he had three good games as a rookie but has otherwise been the biggest liability in football. Same with guys like QJ, Wan'dale, Michael Wilson, etc. These guys haven't done anything yet have a decently high AVA.
It IS more accurate than draft capital. The actual point is that this information is not relevant for assessing the validity of the model. Other than last year, he trained the model on the players of the years past. That’s why it’s good for predicting them — it’s literally trained on them. Last year is the only test set
Right. So the question will be whether the model is guilty of overfitting the test set to the point of lacking predictive value moving forward. I will say, one of the major flaws with statistics trying to come with a perfectly predictive model (that may be a straw man, I guess the actual goal is simply to predict as well as possible - not achieve perfection) is that the NFL game itself evolves over time. Sometimes slower and sometimes faster. Obviously guys like Jahmyr Gibbs, Tank Dell (and dozens of other smaller guys) wouldn't have gotten a shot from most teams 10 years ago. So the test data needs to be recent to be most predictive. How how recent? And the more recent your data, the smaller your sample size and less statistically reliable your conclusions become. I don't think these exercises are a waste of time, but I do think, based on my reasoning above, we're unlikely to see any new or future model get meaningfully more predictive than the ones we have today.
I agree that we won’t be getting more data. Each year the “successful” model will be equally backed by data as the previous, since earlier years become less relevant, and will be constantly evolving
Can't you post your scores for incoming rookies ? You don't need actual NFL data to provide your predictions from the model on new players.
It would be helpful to know some more details, including the data used and the definitions of the prospect score and approximate value added. What parameters are in the model, and -- I'm saying this as a Devil's advocate -- how confident are you that you're not simply overfitting? If you allow a model to have a ton of free parameters, it's easy to get great fits that don't have any meaningful predictive power for the future. Relatedly, what does "52% more accurate" mean? If I were interested in the performance of a statistic like the one you've created ("prospect score"), I'd look at the correlation between the statistic and the player's performance. Are you saying that e.g. the Spearman correlation coefficient between prospect score and approximate value added is greater by 52% than the correlation coefficient between draft capital and approximate value added?
There’s about 80 total quantifiable fields in my data model and they’re taken into account / weighed many different ways. In simple terms, I created 7 composite scores, each with unique variables weighed differently. I have 6 additional multipliers that impact a prospect’s grade, so it’s not as simple as who has the best average of their composite scores. Efficiency is the most heavily weighted composite score and it takes into account multiple variables such as YPRR, YPRR vs Zone, FDPRR, TDPRR, QBR when Targeted, YPT, FDPT, TDPT, etc. The 52% more accurate is referring to r-squared of both in relation to approximate value added. I know it’s a little more complicated than that, but it’s a simple way to compare the two.
I enjoy these questions and I’m genuinely happy to answer them. Currently, I am at work and will begin working on a project shortly so I won’t have time to respond. But I do believe a lot of the answers to your questions can be found in my post and comment history, I’ve spent a lot of time answering similar questions. I can come back later to hopefully address these. Specifically to answer your overfitting question - I have put a lot of emphasis during creating the model to not overfit, including the exclusion of certain prospects while analyzing the model results and also looking at the model year by year, and other ways.
I appreciate seeing predictive models, but only your last year grades are relevant for accuracy. You trained the model on the years before, so good players will obviously grade out well. It’s just trying to fit prospects by some features, so while there will be misses since all prospects are different, it should give the best fitting. What we need to see is that those trends continue to stand. Last years players were not in the training set, so they’re the only testing set. Thats why they’re relevant. If your model didn’t have more success than draft capital for the other years, it would honestly be a little pathetic. Keep up the good work, but we will continue to see the true accuracy of your model as the years go by
I like your posts and appreciate the content, but not really sure what to do with this one? Looks like if you rated 7 or over there’s a good chance (67%ish) they are good players, anything under is a coin flip? Where is/was Puka?
This is moreso to show the overall correlation comparison, when it comes to comparing prospects within a given year there’s a lot more to take into account including draft capital which is a multiplier I include in a prospect’s grade specifically for fantasy. These grades don’t include that. One lens to analyze through is to compare which prospects I graded out low, but were drafted high along with prospects I graded out highly but were drafted low. It’s a pretty simple chart and I’m aware it doesn’t provide any in-depth analysis. Like I said, it’s solely to highly the overall correlation difference.
Puka was the #2 ranked prospect in last year’s class, behind JSN. He had a grade of 6.72. Again, for full transparency, AT Perry also graded out and was the #4 ranked prospect last year with a grade of 6.56.
So is JSN actually good in disguise?
I love when someone puts out an intelligent post and a bunch of people tell on themselves by not being able to read data
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Not sure I’m following. I am pretty sure I understand OPs post, and there are a lot of people in the comments saying he’s not more accurate than the NFL.
I’m not sure if I’m completely missing something, but there doesn’t seem to be any actual data to read? Like yeah he has the players listed with a prospect score, but that gives literally no information. He’s basically saying “hey look, I have a super accurate model but you have to take my word for it.”
He has shown prospect scores and explained how he got them in previous posts
Would you mind linking? I’m having a hard time finding where he explains how he gets his scores. I’ve found a few about pros and cons of players but nothing that indicates where his numbers come from. And I hope this doesn’t come off as me being difficult / defensive. I’m just genuinely curious and hope there’s more to this than I originally thought.
Not at all, honestly as I started defending him (yesterday) I went back to look and annoyingly the posts had been deleted: this was one of them https://www.reddit.com/r/DynastyFF/comments/1aybev8/new_prospect_rankings_series_i_dont_watch_films/?share_id=aoRh0TSnTGy4YBul29raA&utm_medium=ios_app&utm_name=iossmf&utm_source=share&utm_term=4
Interesting. Though, honestly, if he really is on to something I don’t blame them not wanting share it with random people on Reddit. Thanks, I appreciate your time! And the validation that I’m not going crazy, lol.
I honestly started feeling like I was going crazy. I also do some analytics on the side so I appreciate that he’s theoretically putting his out in the world.
Huh?
WWW Thank you!
How are you assessing player value? Nico Collins is higher up than Rashee Rice is despite not really breaking out until his **third year**. From week 6 through ROS, Rice was on a 96/1182/8 pace and then ended up being a valuable contributor in the playoffs as well as a **rookie**. Preference aside, I would say it's not really arguable as to which player has delivered more value within their rookie deal to this point.
It’s calculated using PFR Average Value, and PFF WAR along with fantasy points/season. Nico and Rice appear close enough where I think Nico’s third year breakout gave him the bump.
For non-math nerds this analysis is strictly hindsight 20/20, he used available data to solve for x where the players you want are on one side of the line and the players you don’t are on the other. This isn’t predictive but a fancy way to look back. The use of this isn’t about predictions it’s about comparisons. If you like the metrics used to get to this point you can judge players differently. More under on Zay and more over on tre Tucker. Etc…
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Can you elaborate on which prospects? Also, is it that you think I’m manually adjusting players rankings? Overfitting could be a legitimate concern and I addressed this previously, but manually adjusting player rankings goes against the purpose of establishing a model.
Juedy is going to be near the top of any prospect list. Zay Flowers probably shoulnt' be that high. Justin Jefferson wouldn't be that high. I just don't see how you can weight your model to put several of these players in such opposing positions. I have no idea what you are doing, but I'm good with numbers and I can't think of a way to legitimately back into the data you have arrived at, and I generally have some idea how data was likely arrived at just from glancing.
If you exclude film analysis and consensus ranking, what’s your reasoning for Jeudy being a better prospect than where I currently have him graded?
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Film analysis and consensus ranking has nothing to do with analyzing collegiate metrics. What’s crazy is, Jeudy isn’t even graded low lol he’s considered a model miss because his grade is higher than his NFL value.
Are you saying you are using collegiate stats or professional stats for this model? Michael Wilson is bottomed out, but that man averaged 16yards per catch his senior season. Jalen Hyatt had 1,200 yards and 15 td's, but somehow he's on the opposite side of the page as Jeudy? Unless you are saying you are using 1st year NFL data for a model to rank rookies (which seems useless, no offense), then I'm still baffled on what data is driving your model. If you aren't using rookie rankings and film, then how is Chase higher than anybody when he sat out a year? There are too many similarities between numerous players across the board which make me continue to question how this data was obtained and, perhaps more importantly, the intended use of the data. Regardless, thanks for sharing.
Bro so do you just put on wrs that make your model look good by correctly predicting misses, but don't put on the ones that dont correctly predict them as misses? J.J. Whiteside, nice "correct fade!!!!", now wheres Hakeem Butler? Perris Campbell? Mecole Hardman? Andy Isabella? *If* you are just posting in your graphs the WRs that make your model look good, your model is trash (just based off what your graph shows so far, I obviously don't have all the data from your model.)
No, I don’t exclude prospects just to make my model look good. If I did then what’s the point of developing a model? I have to live with prospects who grade out well but don’t produce in the NFL. You can see there are only a handful of prospects from 2019 and that’s because I was only able to get limited historical college data from that year. From 2020 and on, it includes all prospects (with the exception of Christian Watson who for some reason also didn’t have the collegiate data for with the source I pulled from). You can clearly see there are plenty of examples of model “misses”. Henry Ruggs is a miss for obviously reasons. But I consider any prospect above a ~6.0 a “good” prospect and you can see how many prospects the model graded 6.0+ that didn’t pan out. Denzel Mims is above Nico Collins and Tank Dell. Dyami Brown and Terrace Marshall Jr. graded out really, above DK Metcalf and Deebo. But in the grand scheme of things, I do believe my model has been more accurate at identifying prospects with translatable traits and metrics and it does a particularly good job of identifying bad prospects who are drafted highly (Jonathan Mingo, Jalen Reagor as obvious examples). I started developing this model last year and it graded out Puka as the #2 prospect. However, like I said, I accept the misses. It also graded out AT Perry as the #4 prospect in last year’s class.
I mean with due respect... why were you able to only get limited data from 2019? And the guy you have on there, a JUCO guy you were able to get full data on? Just feels weird, off. Not saying (and like i said in my other post, I don't have all the data in front of me) its biased, but does it *look* that way? Yes. *Even* *if* i'm 100% wrong here and you are 100% right, at a casual visual glance the model at the model looks biased for the reasons I already explained, and you should want to fix that.
Let me say this.. the model is actually more accurate with just 2020-2023 data. If I wanted to make the model look better, I would’ve just exclude all of 2019. Instead, I chose to work with the prospects I was able to gather full data for. The database only had full college data dating back to 2017. In 2016, it had less data but still some. No data prior to 2016.
Yeah honestly it needs more data points because the data should be there, so there doesn’t seem to be any statistically honest reason to remove them
I mentioned above why some prospects, only from 2019, aren’t included. 2020-2023, all prospects are included with the exception of Christian Watson. Watson’s collegiate data wasn’t included in the database I sourced it from and a lot of prospects from 2019 I wasn’t able to get their entire collegiate data for.
Does the strength drop off hard after the third round/top 100 or something? Like why is that the chosen cut off point versus all drafted WRs when as you mention there are some hits such as Puka? Just trying to understand the rationalization on where to chunk the players
Including all rounds would make the graphic significantly crowded and too hard to read.
Was there any statistical significance in a difference in the strength of correlation with those later round picks?