boilermaker5 Posted March 6 Posted March 6 4 hours ago, jchapman said: Plus, wrestling is a timed event, whereas volleyball is a first to so many points event. So matches that are close in score at the end can reflect a strategy of the leading wrestler allowing the losing wrestler to score meaningless points as time runs out. There is no such strategy in volleyball. Hi Pablo! I think jchapman has a good take when comparing results. To me, wrestling is much more binary win/loss, and that is probably the best indicator. Some wrestlers can win a LOT of matches 4-1, whereas others have much more offence. When trying to apply an algorithm based on score, you would think that those who shoot/score a lot would be better (wins by Major/TF more), but in reality/head to head, that one takedown wrestler who is almost impossible to score on may be favored. When trying to predict a win/loss head to head, scoring margins like that aren't going to be the case. If I had to guess, the most predictive model will weigh the win/loss more heavily in wrestling than in volleyball. I would be very interested to see what is more accurate in the tournament, this Pablo model, an ELO model, or rankings like Intermat, and that shouldn't bee TOO difficult of a comparison to make, who is ranked higher on what model, and was the prediction correct. The only problem there will likely be a small sample size (models will likely favor the same wrestler in most cases). Good luck! and finally, you probably have, but have you looked at wrestlestat? they've got a very similar model going: https://www.wrestlestat.com/faq#:~:text=The algorithm factors in the,that have fewer total matches.
cowcards Posted March 6 Posted March 6 Great work Pablo, I'm always a fan of more data. I would love to see what your model ranks if you didn't weigh pins. I think wrestling is unique in with these elo type models because pins can happen at any time with no regard to ability and that it doesn't matter if you win by 1 or 15. You just have to win. There are plenty of guys that don't stack up points even though they could if it was a first to 15 points instead.
Idaho Posted March 6 Posted March 6 7 hours ago, Pablo said: How did you determine that? I have data that informs the value of a pin. You are making things up. I have determined that an undefeated wrestler who beat another wrestler by major decision is ranked and seeded higher than that other wrestler because of logic and because 100% of all ranking outlets and seeding criteria agree. Sponsored by INTERMAT
jmoney Posted March 6 Posted March 6 11 hours ago, TylerDurden said: Counterpoint: you decided that a pin is more valuable in every situation for your calculation. Wrestling isn't the same as volleyball. A pin can happen for reasons other than domination - people get caught while leading, etc. In volleyball, you can't get caught. If you lose a set 25-14, you got smacked. I think that's where your system will fall apart when trying to make it work for wrestling. Good point- wrestling is very challenging to rank because injuries are so common and influence records impactfully. I'd argue using prior All-American and NCAA match performances would be helpful but then you'd have to compare weight class changes, health histories, the amount of data needed to accurately assess this stuff borders on the most ridiculous manual labor ever. I recently spent 8 hours analyzing who was the best pound for pound wrestlers. Here is what I came up with; it was exhausting. I accidentally called Bonus victories "majors" in my rankings. 1.Gable Steveson (data adjusted for match count) Top 40 Majors 5 Top 15 Wins 5 Best Win Ranking 7 Tech Rank 1 Team Points Rank 3 Top 15 Majors 5 Bonus Rate 100 Best Major Ranking 7 2. Mitchell Mesenbrink Top 40 Majors 7 Top 15 Wins 1 Best Win Ranking 2 Tech Rank 1 Team Points Rank 3 Top 15 Majors 1 Bonus Rate 100 Best Major Ranking 2 3. Parker Keckeisen Top 40 Majors 8 Top 15 Wins 5 Best Win Ranking 4 Tech Rank 3 Team Points Rank 5 Top 15 Majors 3 Bonus Rate 90 Best Major Ranking 4 4. Carter Starocci Top 40 Majors 5 Top 15 Wins 4 Best Win Ranking 3 Tech Rank N/A Team Points Rank 2 Top 15 Majors 3 Bonus Rate 94 Best Major Ranking 9 5. Stephen Buchanan Top 40 Majors 8 Top 15 Wins 7 Best Win Ranking 3 Tech Rank N/A Team Points Rank 4 Top 15 Majors 3 Bonus Rate 79 Best Major Ranking 9 6. Wyatt Hendrickson Top 40 Majors 6 Top 15 Wins 4 Best Win Ranking 5 Tech Rank N/A Team Points Rank 1 Top 15 Majors 2 Bonus Rate 89 Best Major Ranking 5 7. Josh Barr (1 loss to 1st ranked) Top 40 Majors 7 Top 15 Wins 4 Best Win Ranking 2 Tech Rank N/A Team Points Rank 8 Top 15 Majors 2 Bonus Rate 83 Best Major Ranking 7 8. Jesse Mendez (1 loss to 1st ranked) Top 40 Majors 6 Top 15 Wins 7 Best Win Ranking 5 Tech Rank N/A Team Points Rank N/A Top 15 Majors 4 Bonus Rate 75 Best Major Ranking 6 9. Dean Hamiti Top 40 Majors 7 Top 15 Wins 4 Best Win Ranking 10 Tech Rank N/A Team Points Rank 10 Top 15 Majors 2 Bonus Rate 74 Best Major Ranking 10 10. Michael Caliendo (1 loss to 1st ranked) Top 40 Majors 8 Top 15 Wins 5 Best Win Ranking 7 Tech Rank N/A Team Points Rank N/A Top 15 Majors 2 Bonus Rate 74 Best Major Ranking 9 11. Max McEnelly Top 40 Majors 3 Top 15 Wins 4 Best Win Ranking 7 Tech Rank 3 Team Points Rank 7 Top 15 Majors 2 Bonus Rate 83 Best Major Ranking 9 12. Keegan O’Toole Top 40 Majors 3 Top 15 Wins 2 Best Win Ranking 2 Tech Rank N/A Team Points Rank N/A Top 15 Majors 1 Bonus Rate 92 Best Major Ranking 10 13. Shane Van Ness (1 loss to 4th ranked) Top 40 Majors 5 Top 15 Wins 4 Best Win Ranking 3 Tech Rank N/A Team Points Rank N/A Top 15 Majors 3 Bonus Rate 78 Best Major Ranking 3 14. Matt Ramos Top 40 Majors 2 Top 15 Wins 5 Best Win Ranking 3 Tech Rank 2 Team Points Rank 6 Top 15 Majors 1 Bonus Rate 78 Best Major Ranking 11 15. Caleb Henson Top 40 Majors 3 Top 15 Wins 3 Best Win Ranking 4 Tech Rank N/A Team Points Rank N/A Top 15 Majors 2 Bonus Rate 75 Best Major Ranking 14 16. Greg Kerkvliet Top 40 Majors 2 Top 15 Wins 3 Best Win Ranking 11 Tech Rank N/A Team Points Rank N/A Top 15 Majors 1 Bonus Rate 81 Best Major Ranking 15 17. Owen Trephan Top 40 Majors 3 Top 15 Wins 2 Best Win Ranking 6 Tech Rank N/A Team Points Rank 9 Top 15 Majors 0 Bonus Rate 78 Best Major Ranking 28 18. Brock Hardy (3 losses to 1st, 8th & 9th ranked) Top 40 Majors 4 Top 15 Wins 4 Best Win Ranking 5 Tech Rank N/A Team Points Rank N/A Top 15 Majors 2 Bonus Rate Best Major Ranking 5 19. Terell Barraclough (1 loss to 30th ranked) Top 40 Majors 3 Top 15 Wins 5 Best Win Ranking 4 Tech Rank N/A Team Points Rank N/A Top 15 Majors 0 Bonus Rate 58 Best Major Ranking 18 20. Isaac Trumble (3 losses to 2nd, 2nd and 6th ranked) Top 40 Majors 7 Top 15 Wins 4 Best Win Ranking 6 Tech Rank N/A Team Points Rank N/A Top 15 Majors 1 Bonus Rate 68 Best Major Ranking 6 21. Peyton Hall (1 loss to 3rd ranked) Top 40 Majors 4 Top 15 Wins 4 Best Win Ranking 7 Tech Rank 9 Team Points Rank N/A Top 15 Majors 0 Bonus Rate 59 Best Major Ranking 17 Honorable Mention: Mullen, Cardenas, Alirez, Kasak, Plott, pinto, Ferrari, Beard, Haines, little, Stout
Ragu Posted March 6 Posted March 6 I’m basing all further arguments and points of data upon the Pablo rankings system and I urge you all to follow suit
Gene Mills Fan Posted March 6 Posted March 6 He may yet determine and assign value to the correct variables. I applaud his passion! I also enjoy Spanish rice and would like to get his recipe.
Pablo Posted 19 hours ago Author Posted 19 hours ago Hey gang - I exceeded my quota post last week and so got locked out of the discussion on Wednesday, so sorry for cutting out. What I did in the meantime is jumped into completing the dataset and upgrading analysis. I believe I have now gotten ALL of the matches for all the D1 wrestlers. I went through every team's schedule on Track and downloaded all the results from every event that had matchups of D1 wrestlers. I also have a ton of matches of non-D1 wrestlers, but they are not quite as complete. If I found an event that had maybe 1 D1 squad there (maybe JV) and the all the opponents were non-D1, I tended to pass by it. But if there were matchups between D1 wrestlers mixed in, I did always grab all of them, even if they weren't D1 opponents. As a result, I do have a lot of guys who are from non-D1 school or even unattached to a school who have matches in the database. One thing I did to make life more manageable was to cull all the matches where it was a non-D1 wrestler who only showed up once. These matches do not contribute to the rankings, and just lead to a lot more baggage. That was something like 1200 matches that I identified. I spent the past week downloading all the scores, formatting them into a style I can use, and then cleaning them up. You gotta get rid of all the byes and the injury defaults and the medical forfeits. Also, while it is convenient to use all the scores from trac, depending on the details they don't always format like they should. Moreover, sometimes the input data is weird, because you get guys who are sometimes listed as wrestling for, for example, Navy, but other times they are listed for the Navy Wrestling Club (depending on the event). So I had to go through all the wrestlers and find the ones who were listed on multiple "teams" and consolidate them onto a single card (my goal here is to evaluate wrestlers, so I don't worry about whether events count toward the NCAA or not; as long as they are on the mat trying, I include their results). The other issue is that they get entered in, depending on who is entering, with different names, so is it Josh or Joshua? Well, they are the same guy, so I tried to find all the places where they had different names for the same wrestler. I finally got it done yesterday afternoon. The final result is a database of nearly 17000 wrestling matches with more than 3800 wrestlers. This is massive. But I've get things set up to calculate. It will take a while, and there are some things to work on (I still don't have conference tournaments added yet, but I'll get there). But in the meantime, there are things to learn... 1 1
Pablo Posted 19 hours ago Author Posted 19 hours ago The first thing I did with the dataset is to look at matches I can use for predictive value. Fortunately, I have been able to find almost 1000 cases where the same wrestlers have met up twice during the season. This is awesome, because I can use that to get some insight into how to use match outcomes (btw, although 1000 sounds like a lot, that's not nearly enough to get a really good analysis; for my volleyball rankings, my match pairs dataset consists of nearly 100K example! But to be fair, in volleyball, you have conference teams playing home/home every year (and I have almost 20 years worth of data) and I actually have all the matches for all college levels (D1, D2, D3 and NAIA); unless something changes massively in how scores are available, that ain't happening in wrestling) But I have been able to learn from what I have. For example: If two wrestlers meet up twice in the season, the probability that the same wrestler wins both times is....82.8% That's a little higher than you usually see in other places. In NFL, it's like 63%. In college volleyball, it's about 78%. If you break that down, what it tells us is that the "better" wrestler wins about 91% of the time on average. That's pretty good, because when you say, "on average" it includes those matches of Gable Stevenson vs me and those matches of Gable Stevenson vs Gable Stevenson with a toothache. There are a couple of ways to interpret this result. It could be that the average match just isn't that close, and the one guy is much better than the other. The other interpretation, which I think is probably closer to reality, is that the standard deviation of quality is really low. You can see how this comes into play if you compare something like volleyball and tennis. I mentioned that volleyball has like a 78% repeat win rate, but in women's tennis, which I've looked at in the past, it's less than 70%. The great thing about these two sports is that they both have enough points and so the results are pretty easy to model. Volleyball works, and the real outcomes very closely reflect the results of a simulated model. In fact, that was my big breakthrough with Pablo rankings, figuring out the model that relates scoring to winning. Thinking that tennis should be similar, I gave that a try, and it really didn't work. Everything was there, but the performances just didn't match up. I concluded that the problem with tennis is that the standard deviation of quality is a lot higher. For starters, you have things like different surfaces, that affects how good players are, and the other problem is that there was only one competitor. In volleyball, with 6 players, if one player has a bad night, it can be offset by another player having a good night. If a tennis player has a bad day, they are on their own. I wondered if that would be an issue in wrestling, but apparently it is not. Even though it is a single competitor, it does not appear that the quality of their wrestling ability varies as much as you might see in other sports. That's thing 1 I have learned. Hopefully, this will bode well toward predictability (although I will add, I did take a look at the Pac12 tournament results; 25 rematches in the tournament, the wrestler who won during the regular season only won 17 (68%? Ouch!) Past performance does not guarantee future success....(FWIW, my Pablo rankings last week correctly predicted 18 of those matches :)) More to come 1
Pablo Posted 18 hours ago Author Posted 18 hours ago Even though the simple "did the same person win twice" answer is 82.8, the dataset is big enough to look more closely. For example, what about the different outcomes For example, let's start with a TF. If a wrestler wins by Tech Fall, that wrestler wins the other match 88% of the time. That makes sense, because tech falls are more likely to happen when one wrestler is much better than the other. What it says is that a wrestler who wins by a tech basically has a 94% chance of winning against that opponent. It's interesting, though that if a wrestler beats a guy by TF, if they come back and wrestle again, there is more than a 10% chance that the other guy wins. As upset rates go, that's pretty darn high considering we consider a TF to be pretty dominating Let's go to a Major decision. One thing I am seeing in the data is that major decisions are about as good as TFs when it comes to prediction, again, 88%. Now, I do see some correlation with point difference, so this might be a case where the real line between a dec/major/TF doesn't correlate with the point cutoffs in the rule book, but I will look at that later. In other words, it may be that up until, say, 10 points, the point difference reflects the quality difference, but above 10, it's all the same. I think that is the basic idea, although I don't know where that line is yet. Before going to a regular decision, let's look at overtime. In fact, if the match goes into overtime, there is only a 54% chance that the same guy wins twice. At this point, it's not much more than a coin flip. I don't think that is unreasonable. For a regular decision, it's 77%. So if a wrestler wins by a regular decision, there is almost a 1/4 chance he will lose if they wrestle again. 1
Wrestleknownothing Posted 18 hours ago Posted 18 hours ago There is a posting limit? Drowning in data, but thirsting for knowledge
Pablo Posted 18 hours ago Author Posted 18 hours ago And the one you are interested in, the pin. With this data, I have a better idea of how to interpret that. Let's start at the beginning. Now that you've seen the value of all these other events in terms of predictability, how to interpret a pin? What do you think is the probability of a wrestler who wins by pin winning again? Is it comparable to a TF? Major? Dec? In fact, the answer is 81.9%. In other words, it's basically the overall. What that means is that if you know that wrestler A beat wrestler B, it does not help you to know if it is a pin or not. A pin is the equivalent to the average match outcome. Did you see that coming? Personally, I'm not all that surprised. I've always felt that pins at this level can be fluky. You make a mistake and you are in a cradle and gone. BUT there is more to it. The data are noisy (this is getting tough), but there is a real correlation between how fast the pin is. Pins in the first oh, say, 2 minutes are more like tech falls (or better, even, >90%), while pins in the last minute of regulation come in at like 70%? I don't have enough pins in overtime to make a conclusion, but for the 5 matches, it's 3 wins in the rematch, 2 losses. This is all great stuff, and I can use it. It's telling me that the value I was using for a pin previously was much too high (and that was apparent in the results), and so I've set it up to reflect that now. I think this is getting closer to what it needs to. I reran the 125s from last week with the full data and the new model, and the result is below. Remember, this is only taking into account data from before last weekend, so are not the current rankings. My next step is to run the full set of rankings for all weights and then see how it did in the conference tournaments. I'll be back when I have that information. 1 . 125:Purdue_Matt Ramos 2 . 125:Oklahoma St_Troy Spratley 3 . 125:Arizona St_Richard Figueroa 4 . 125:Northern Colorado_Stevo Poulin 5 . 125:NC St_Vince Robinson 6 . 125:Wisconsin_Nicolar Rivera 7 . 125:West Virginia_Jett Strickenberger 8 . 125:Virginia Tech_Eddie Ventresca 9 . 125:Penn St_Luke Lilledahl 10 . 125:Ohio St_Brendan McCrone 11 . 125:Nebraska_Caleb Smith 12 . 125:Iowa St_Kysen Terukina
Pablo Posted 18 hours ago Author Posted 18 hours ago 6 minutes ago, Wrestleknownothing said: There is a posting limit? For new guys, apparently.
CHROMEBIRD Posted 16 hours ago Posted 16 hours ago Wow, the data cleanup sounds like a nightmare. How far back did you go? Or are you talking about the data for this season only? There's (free?) software that can standardize, de-duplicate, and scrub records from a flat file. B2B CRM bolt-ons may be particularly well suited since you have wrestlers (contacts) attached to teams (organizations). I'm also wondering how granular the data from Track is. Like is there enough to produce a wrestling version of KenPom advanced analytics? Or are your rankings meant to be more declarative than predictive?
Gene Mills Fan Posted 16 hours ago Posted 16 hours ago 1 hour ago, Pablo said: And the one you are interested in, the pin. With this data, I have a better idea of how to interpret that. Let's start at the beginning. Now that you've seen the value of all these other events in terms of predictability, how to interpret a pin? What do you think is the probability of a wrestler who wins by pin winning again? Is it comparable to a TF? Major? Dec? In fact, the answer is 81.9%. In other words, it's basically the overall. What that means is that if you know that wrestler A beat wrestler B, it does not help you to know if it is a pin or not. A pin is the equivalent to the average match outcome. Did you see that coming? Personally, I'm not all that surprised. I've always felt that pins at this level can be fluky. You make a mistake and you are in a cradle and gone. BUT there is more to it. The data are noisy (this is getting tough), but there is a real correlation between how fast the pin is. Pins in the first oh, say, 2 minutes are more like tech falls (or better, even, >90%), while pins in the last minute of regulation come in at like 70%? I don't have enough pins in overtime to make a conclusion, but for the 5 matches, it's 3 wins in the rematch, 2 losses. This is all great stuff, and I can use it. It's telling me that the value I was using for a pin previously was much too high (and that was apparent in the results), and so I've set it up to reflect that now. I think this is getting closer to what it needs to. I reran the 125s from last week with the full data and the new model, and the result is below. Remember, this is only taking into account data from before last weekend, so are not the current rankings. My next step is to run the full set of rankings for all weights and then see how it did in the conference tournaments. I'll be back when I have that information. 1 . 125:Purdue_Matt Ramos 2 . 125:Oklahoma St_Troy Spratley 3 . 125:Arizona St_Richard Figueroa 4 . 125:Northern Colorado_Stevo Poulin 5 . 125:NC St_Vince Robinson 6 . 125:Wisconsin_Nicolar Rivera 7 . 125:West Virginia_Jett Strickenberger 8 . 125:Virginia Tech_Eddie Ventresca 9 . 125:Penn St_Luke Lilledahl 10 . 125:Ohio St_Brendan McCrone 11 . 125:Nebraska_Caleb Smith 12 . 125:Iowa St_Kysen Terukina You are a DATA WARRIOR!!
Pablo Posted 16 hours ago Author Posted 16 hours ago 26 minutes ago, CHROMEBIRD said: Wow, the data cleanup sounds like a nightmare. How far back did you go? Or are you talking about the data for this season only? There's (free?) software that can standardize, de-duplicate, and scrub records from a flat file. B2B CRM bolt-ons may be particularly well suited since you have wrestlers (contacts) attached to teams (organizations). I'm also wondering how granular the data from Track is. Like is there enough to produce a wrestling version of KenPom advanced analytics? Or are your rankings meant to be more declarative than predictive? It's like a KenPom approach, although based not so much on "advanced analytics" but certainly analytics. The relevant data that I pull out of the track result is Date, winner, winner score, loser, loser score, fall time (if it's a pin), and then just yes/no flags of TF, SV or other TB. I may have the wherewithall but certainly not the motivation to break it down by scoring events (takedowns, near falls, escape, etc), which would be closer to KenPom. This is more like Sagarin. Predictive? For sure. See my post last week talking about the concept of "prediction" and how it's inherent in the concept of "better" (which includes the wrestler Gable Stevenson with a toothache). The "better" wrestler is more likely to win. This is just this year's data. I spent probably 50 hours downloading and massaging to get it here. I could probably improve that by 20% knowing better what to do , but that's just too much effort to go back to previous years.
Gene Mills Fan Posted 15 hours ago Posted 15 hours ago 29 minutes ago, Pablo said: It's like a KenPom approach, although based not so much on "advanced analytics" but certainly analytics. The relevant data that I pull out of the track result is Date, winner, winner score, loser, loser score, fall time (if it's a pin), and then just yes/no flags of TF, SV or other TB. I may have the wherewithall but certainly not the motivation to break it down by scoring events (takedowns, near falls, escape, etc), which would be closer to KenPom. This is more like Sagarin. Predictive? For sure. See my post last week talking about the concept of "prediction" and how it's inherent in the concept of "better" (which includes the wrestler Gable Stevenson with a toothache). The "better" wrestler is more likely to win. This is just this year's data. I spent probably 50 hours downloading and massaging to get it here. I could probably improve that by 20% knowing better what to do , but that's just too much effort to go back to previous years. Take a lunger break
ugarles Posted 15 hours ago Posted 15 hours ago I appreciate the work, @Pablo. I do not appreciate the generic reaction to the output of thousands of data points compiled, analyzed and reconciled being "your result does not align with these two specific matches i remember." that misalignment doesn't make the data output "wrong" even if you are reluctant to take the advice to Las Vegas. Anyway, my question is ... do the results have a recursive component like ELO? In other words, it's a big deal if Gable pins Gable (toothache) but not such a big deal if Gable pins Pablo. Does the strength of opponent in each additional match matter?
Pablo Posted 14 hours ago Author Posted 14 hours ago 2 minutes ago, ugarles said: I appreciate the work, @Pablo. I do not appreciate the generic reaction to the output of thousands of data points compiled, analyzed and reconciled being "your result does not align with these two specific matches i remember." that misalignment doesn't make the data output "wrong" even if you are reluctant to take the advice to Las Vegas. Anyway, my question is ... do the results have a recursive component like ELO? In other words, it's a big deal if Gable pins Gable (toothache) but not such a big deal if Gable pins Pablo. Does the strength of opponent in each additional match matter? It's all based on an ELO type of approach, but with the finetuning of adding score differentials (which you can't do in an ELO-Chess model). So it is based completely on head-to-head outcomes. The problem with doing something like what you are asking is the system doesn't really know who is "good" and who is "bad" independent of all the other results. If all it knows is that Gable pins Pablo and Gable pins Toothache Gable, then it won't see them as any different. But if it knows that Gable with a toothache has also beaten Kerkvliet and Luffman and Andrews and Swenski, and that Kerkvliet and Luffman and Andrews and Swenski have all beaten Pablo, then yeah, that win over Gable with a toothache means a lot more, and, in fact, that win over Pablo probably doesn't mean much. I haven't shown the information here, but basically what it says is that if wrestler A beats wrestler B by X amount, then that translates into a "rating difference" of Z (where Z is a value that can be transformed into a win probability) between them. We'll call that the "match outcome." What Pablo does is give each wrestler a rating, and then compares the difference of the two ratings for the wrestlers to the "match outcome." So Gable Stevenson has a rating value of 6900, Gable with a toothache is rated 6800, and Pablo is rated 0. Healthy Gable wins by pin, such that the match outcome is 2400, let's say (in order to get there, I'd have to run around and avoid him for more than 2 minutes, in fact). So Pablo would see the differences between the wrestlers (100 and 6800) and compare them to the match outcome. The difference between the match outcome (2400) and the difference between the wrestlers (100) is huge for for Gable vs Gable; as a result, Pablo will try to make the rating difference bigger to try to minimize that deviation. The difference between Gable and Pablo looks huge, but when you program it right, you put in protections that say, "we expect a dominating performance, we got a dominating performance, it's all good" So the match between Gable and Gable means a lot more than does Gable over Pablo. Now do this over 1700 matches for 400 wrestlers at each weight class and find the ratings that minimize the overall error. Even if Gable pins Gable, you can't just set them 2400 points apart without screwing up all the other comparisons that you have. This is why I could toss all the matches where a wrestler has 1 match in the system. The rating for that wrestler will just be X away from the rating of the guy he wrestled and is not anchored to anything else. Whew, that is long! I hope that answers your question. It's very insightftul. 1
Pablo Posted 14 hours ago Author Posted 14 hours ago 24 minutes ago, ugarles said: I appreciate the work, @Pablo. I do not appreciate the generic reaction to the output of thousands of data points compiled, analyzed and reconciled being "your result does not align with these two specific matches i remember." that misalignment doesn't make the data output "wrong" even if you are reluctant to take the advice to Las Vegas. To address this part, to be fair there are kinks in the system to be worked out, and it's through your feedback that I can improve it. Last week first draft I put out certainly had issues in terms of weighting events in the model and, more importantly, a lack of data. I had 5000 data points last week. This week I have 17000. More data helps.
ugarles Posted 13 hours ago Posted 13 hours ago 48 minutes ago, Pablo said: Whew, that is long! I hope that answers your question. i do appreciate the effort of the long explanation but we have always been on the same page. the answer to my question was "yes."
CTMopar Posted 11 hours ago Posted 11 hours ago Mr.Pablo the NCAA Tournament is a double elimination format so what happens in the Championship bracket could and most likely will make a major difference in your calculations when wrestlebacks are factored in!!! P.S. The PSU "Train"!!!
Pablo Posted 10 hours ago Author Posted 10 hours ago 21 minutes ago, CTMopar said: Mr.Pablo the NCAA Tournament is a double elimination format so what happens in the Championship bracket could and most likely will make a major difference in your calculations when wrestlebacks are factored in!!! P.S. The PSU "Train"!!! All matches are taken into account
ugarles Posted 5 hours ago Posted 5 hours ago (edited) have you updated your rankings with the complete data set somewhere? i don't see it. Edited 5 hours ago by ugarles
Adam Waters Faith Christian Academy, Pennsylvania Class of 2026 Committed to Ohio State Projected Weight: 184
Isreal Ibarra Santa Cruz Valley Union, Arizona Class of 2025 Committed to Arizona State Projected Weight: 174, 184
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