Pablo

125 Won Lost Rating 1. 125:Penn St_Luke Lilledahl 24 3 8795 2. 125:Purdue_Matt Ramos 29 3 8781 3. 125:Oklahoma St_Troy Spratley 21 4 8537 4. 125:West Virginia_Jett Strickenberger 11 4 8398 5. 125:NC St_Vince Robinson 13 1 8362 6. 125:Virginia Tech_Eddie Ventresca 19 4 8336 7. 125:Wisconsin_Nicolar Rivera 22 6 8148 8. 125:Nebraska_Caleb Smith 23 5 8025 9. 125:Lehigh_Sheldon Seymour 19 5 7800 10. 125:Arizona St_Richard Figueroa 14 3 7745 11. 125:Northern Colorado_Stevo Poulin 19 6 7659 12. 125:Rutgers_Dean Peterson 19 7 7574 13. 125:Princeton_Marc-Anthony McGowan 15 4 7491 14. 125:Ohio St_Brendan McCrone 18 8 7414 15. 125:South Dakota St_Brady Roark 6 0 7352 16. 125:Indiana_Jacob Moran 24 10 7274 17. 125:North Carolina_Spencer Moore 15 8 7240 18. 125:Northern Illinois_Blake West 25 6 7195 19. 125:Nebraska_Kael Lauridsen 5 3 7152 20. 125:Oklahoma_Antonio Lorenzo 17 11 7106 21. 125:Oregon St_Maximo Renteria 13 7 7078 22. 125:Pennsylvania_Max Gallagher 22 10 7060 23. 125:Northern Iowa_Trever Anderson 14 11 7044 24. 125:Penn St_Kurt McHenry 7 3 7039 25. 125:Nebraska_Alan Koehler 8 4 6987 26. 125:Minnesota_Cooper Flynn 14 7 6936 27. 125:Oklahoma_Beric Jordan 11 2 6918 28. 125:West Virginia_Jace Schafer 7 3 6894 29. 125:South Dakota St_Tanner Jordan 16 9 6874 30. 125:Ohio St_Vinny Kilkeary 5 1 6789 31. 125:Iowa St_Kysen Terukina 11 2 6757 32. 125:Campbell_Anthony Molton 18 7 6696 33. 125:Missouri_Gage Walker 9 11 6692 34. 125:Indiana_Blaine Frazier 14 5 6638 35. 125:Army_Charlie Farmer 22 10 6600 36. 125:Oklahoma St_Sam Smith 5 1 6576 37. 125:Cornell_Greg Diakomihalis 9 0 6519 38. 125:Northwestern_Dedrick Navarro 18 11 6505 39. 125:North Dakota St_Ezekiel Witt 3 3 6478 40. 125:North Dakota St_tristan daugherty 10 16 6477 41. 125:Iowa St_Ethan Perryman 12 9 6466 42. 125:Princeton_Ethan Rivera 9 7 6344 43. 125:Pittsburgh_Nick Babin 8 10 6326 44. 125:Iowa_Joey Cruz 12 14 6306 45. 125:Campbell_Cooper Shore 20 3 6304 46. 125:Utah Valley_Bridger Ricks 12 12 6280 47. 125:Harvard_Diego Sotelo 11 5 6209 48. 125:Army_Nain Vazquez 8 3 6207 49. 125:Lehigh_Mason Ziegler 6 2 6197 50. 125:Northern Iowa_garret Rinken 11 5 6128 51. 125:Binghamton_Carson Wagner 16 9 6127 52. 125:Northern Illinois_Camren French 7 1 6126 53. 125:Virginia_Keyveon Roller 13 8 6119 54. 125:PRTC_Davis Motyka 11 2 6067 55. 125:CSU Bakersfield_Richard Castro-Sandoval 13 9 6064 56. 125:Utah Valley_Talen Eck 5 3 6011 57. 125:Cal Poly_Koda Holeman 14 12 6004 58. 125:Pittsburgh_Matt Marlow 8 4 5994 59. 125:Northern Iowa_Kyle Gollhofer 7 7 5989 60. 125:Cornell_Marcello Milani 12 10 5927 61. 125:Missouri_Mack Mauger 14 8 5915 62. 125:Wyoming_Garrett Ricks 4 6 5819 63. 125:Purdue_Isaiah Quintero 6 6 5815 64. 125:Iowa St_Adrian Meza 9 5 5814 65. 125:Drexel_Desmond Pleasant 11 6 5809 66. 125:Gardner-Webb_Tyson Lane 6 7 5790 67. 125:Minnesota_Blake Beissel 4 4 5764 68. 125:The Citadel_Gylon Sims 20 8 5744 69. 125:South Dakota St_Daniel Guanajuato 5 2 5706 70. 125:Utah Valley_Yusief Lillie 4 3 5704 71. 125:SIU Edwardsville_Drew Davis 11 7 5693 72. 125:Michigan St_Caleb Weiand 12 17 5676 73. 125:Pennsylvania_Brady Pruett 9 6 5670 74. 125:Appalachian St_Noah Luna 5 6 5665 75. 125:Minnesota_Brandon Morvari 4 5 5630 76. 125:Air Force_Bubba Wright 8 8 5610 77. 125:Purdue_Ashton Jackson 21 9 5574 78. 125:Columbia_Sulayman Bah 15 14 5559 79. 125:American_Jay Peace 14 6 5553 80. 125:Clarion_Travis Clawson 12 11 5536 81. 125:SIU Edwardsville_Davian Guanajuato 9 10 5459 82. 125:LIU_Robert Sagaris 12 9 5438 83. 125:The Citadel_Malik Hardy 8 6 5393 84. 125:Northern Iowa_Brandon Paez 5 4 5333 85. 125:Harvard_Logan Brzozowski 6 7 5320 86. 125:Kent St_Nico Calello 13 10 5310 87. 125:Lock Haven_Sean Logue 8 4 5309 88. 125:Edinboro_Christopher Vargo 18 13 5297 89. 125:Northern Iowa_Bowen Downey 7 5 5270 90. 125:Bellarmine_Jack Parker 16 11 5267 91. 125:Central Michigan_Kaden Chinavare 6 9 5250 92. 125:Cleveland St_Ben Aranda 12 12 5152 93. 125:Little Rock_Reid Nelson 3 3 5118 94. 125:Pittsburgh_Colyn Limbert 12 8 5112 95. 125:Rutgers_Ayden Smith 12 4 5105 96. 125:Franklin & Marshall_James Garcia 12 8 5094 97. 125:Cal Baptist_Mitchell Neiner 8 16 5093 98. 125:Illinois_Caelen Riley 5 14 5091 99. 125:Utah Valley_Will VomBaur 5 1 5051 100. 125:Michigan_Nolan Wertanen 5 6 5010 101. 125:Franklin & Marshall_Jack Parker 17 15 5003 102. 125:Rider_Noah Michaels 14 9 4997 103. 125:Gardner-Webb_Jeffrey Jacome 10 13 4982 104. 125:George Mason_Ben Monn 15 7 4966 105. 125:Appalachian St_Bryson Terrell 11 12 4947 106. 125:Wisconsin_Wyatt Skebba 3 9 4888 107. 125:Findlay_Billy Smith 6 1 4872 108. 125:LIU_Sawyer Ostroff 3 5 4870 109. 125:North Carolina_Cameron Stinson 10 6 4866 110. 125:Air Force_Nico Tocci 3 7 4814 111. 125:Navy_Nick Treaster 11 11 4805 112. 125:American_Coen Bainey 13 14 4732 113. 125:Michigan_Christian Tanefeu 6 12 4724 114. 125:Bellarmine_Damion Ryan 8 11 4723 115. 125:Maryland_Tyler Garvin 6 15 4677 116. 125:Appalachian St_Colby McBride 8 6 4647 117. 125:Little Rock_Jayden Carson 2 10 4616 118. 125:Pittsburgh_Tyler Chappell 7 9 4602 119. 125:Virginia_Anthony Rossi 10 6 4557 120. 125:Bellarmine_Shay Korhorn 3 4 4542 121. 125:Army_Caleb Uhorchuk 7 10 4532 122. 125:Cornell_Joseph Sciarrone 5 3 4514 123. 125:Central Michigan_Grant Stahl 7 15 4511 124. 125:Buffalo_Max Elton 18 21 4486 125. 125:The Citadel_Tyler Washburn 5 6 4482 126. 125:Little Rock_Tyson Roach 5 5 4431 127. 125:Rider_Kaden Naame 4 7 4401 128. 125:The Citadel_Genaro De La Garza 3 3 4387 129. 125:George Mason_JB Dragovich 16 10 4310 130. 125:Illinois_Anthony Ruzic 1 5 4281 131. 125:Maryland_Abram Cline 13 13 4213 132. 125:Missouri_Luqman Masud 4 5 4203 133. 125:Unattached_Kaedyn Williams 5 6 4190 134. 125:Ohio University_Malachi O`Leary 5 8 4184 135. 125:Iowa_Dru Ayala 4 5 4171 136. 125:Edinboro_Landon Bainey 7 8 4158 137. 125:Oregon St_Gage Singleton 5 2 4138 138. 125:Navy_Isaac Hampton 4 7 4137 139. 125:West Virginia_Matthew Dolan 10 9 4080 140. 125:Chattanooga_Elijah Lowe 7 6 4058 141. 125:Franklin & Marshall_Ejiro Montoya 15 12 4038 142. 125:Chattanooga_Tyler Tice 10 21 4028 143. 125:Michigan St_Drew Hansen 7 9 4004 144. 125:Binghamton_Jay McDonnell 4 4 4003 145. 125:Michigan_Wilfred Tanefeu 0 6 3985 146. 125:Hofstra_Dylan Acevedo-Switzer 6 15 3978 147. 125:Kent St_Tyeler Hagensen 10 19 3961 148. 125:Michigan St_Nick Corday 8 8 3960 149. 125:Clarion_Robert Gardner 3 6 3920 150. 125:Wyoming_Tucker Bowen 3 4 3884 151. 125:Lehigh_Ethan Smith 4 4 3875 152. 125:Minnesota_Quincy Hulverson 1 6 3851 153. 125:Rutgers_Kurt Wehner 4 3 3849 154. 125:Army_Noah Tonsor 4 6 3846 155. 125:Clarion_Weston Pisarchick 5 7 3781 156. 125:Ohio University_Ryan Meek 2 14 3760 157. 125:Lock Haven_Lucas Fye 3 9 3717 158. 125:Northern Illinois_Talan Parsons 5 8 3663 159. 125:Harvard_Isaiah Adams 3 5 3640 160. 125:Navy_Jack Bergmann 3 7 3594 161. 125:SIU Edwardsville_Deion Johnson 1 7 3579 162. 125:Maryland_Presden Sanchez 8 11 3576 163. 125:Hofstra_Teague Strobel 6 12 3573 164. 125:Lehigh_Logan Wadle 3 7 3558 165. 125:Northern Colorado_Bryson Valdez 2 4 3496 166. 125:Ohio University_Logan Dean 2 6 3492 167. 125:Chattanooga_Easton Cooper 8 8 3421 168. 125:Morgan St_Treshaun Tecson 3 4 3409 169. 125:Indiana_Anthony Isek 3 4 3394 170. 125:Bellarmine_ostin blanchard 1 7 3379 171. 125:Lock Haven_Branden Wentzel 1 7 3362 172. 125:Buffalo_Aiden Shufelt 7 9 3257 173. 125:North Dakota St_Kody Tanimoto 0 7 3202 174. 125:Duke_Riley Rowan 4 8 3013 175. 125:Brown_Jared Brunner 2 21 2962 176. 125:George Mason_Gunner Chambers 4 8 2962 177. 125:Edinboro_Eamonn Jimenez 5 16 2950 178. 125:Duke_Ethan Grimminger 3 9 2916 179. 125:Edinboro_Austin Zimmerman 8 16 2865 180. 125:Lock Haven_Alex Reed 3 6 2727 181. 125:Davidson_Luke Passarelli 6 13 2675 182. 125:Franklin & Marshall_Charlie Colantonio 6 8 2620 183. 125:Morgan St_Julian Dawson 5 15 2593 184. 125:Rider_Brady Klinsky 1 8 2499 185. 125:VMI_Cody Tanner 6 12 2470 186. 125:Presbyterian_Brayden Adams 3 14 2469 187. 125:Bellarmine_Micah medina 1 6 2284 188. 125:VMI_Waylon Rogers 5 15 2208 189. 125:Columbia_Connor Smith 1 6 2140 190. 125:Rider_Alex Esposito 1 12 2095 191. 125:Bucknell_Chris Nucifora 0 9 2008 192. 125:Bucknell_Grayson McLellan 2 7 1994 193. 125:Davidson_Enis Ljikovic 3 12 1886 194. 125:Seton Hill_Jacob Sombronski 1 6 1882 195. 125:Cleveland St_Parker Pikor 3 7 1796 196. 125:Iowa_Anthony Lavezzola 0 6 1750 197. 125:Bloomsburg_Major Lewis 1 8 1662 198. 125:Edinboro_Caleb Edwards 0 6 1568 199. 125:Franklin & Marshall_Vincent Gioffre 1 6 1128 200. 125:Gardner-Webb_Ty Porter 1 6 970 201. 125:Sacred Heart_Michael Baker 0 8 -995 202. 125:Bucknell_Meyer Rosen 0 8 -2068

Here are the final Pablo rankings for the 2024-2025 season. These rankings include every wrestler I have in my database that has at least 6 matches. For non-D1 wrestlers, that means they have at least 6 matches against D1 competition. For D1 wrestlers, they just have to have 6 against someone. For reference, I have included the records of the wrestlers that I have in the dataset. This includes everyone, including redshirts. These may not match the official records you will see in other places. For starters, I don't know that I have everything. Second, I'm not including things like medical forfeits. So they aren't going to be match up perfectly, but it's pretty close. Here we go...

What I've seen in my analysis of rematch data (when two wrestlers come back and wrestle again) is that pins in like the first 90 seconds mean more but after that 90s point, it all means the same. So, at least at the college level, quick pins result from the big difference between wrestlers, but late pins look to be less indicative. At least that's where it is now. I need to add the national tournament matches to my match pairs. However, right now I'm chugging along calculating end of season rankings. Hopefully I will have them tomorrow.

"I have that all written up here in a pie graph here. And over here I have a rubber chicken graph...." - Steve Martin

The dynamic of the relationship between MDs and TFs is interesting. I've seen this in Pablo analysis. As the difference between the wrestlers increases, we see more MDs and TFs. But then as the difference gets even bigger, then the numbers of TFs goes up significantly, and the number of MDs goes down. So TFs come at the expense of MDs. I have seen a little increase in pins as the difference between them goes up, but it's not huge. So there isn't a lot of trade off of pins and TFs.

I was thinking that was the case in the early 80s but I was pretty young and clueless back then

Didn't it used to be that if the first period ended 0-0 that someone had to get a stalling warning?

You ask a good question. What I've done in the past (in volleyball) is to use previous year data at the beginning of the season until there is enough data in the current season to make reliable rankings. That usually takes about 4 - 6 weeks of competition in volleyball, although that's a more compact season (so it goes through about 12 matches before I move on). With wrestling however, I don't know how it is going to work. In order to do the weighting correctly in volleyball, i needed several seasons worth of results in order to see how much last season informs this season. I've got one season's worth of data now. Even aside from that, the challenges in wrestling are multifold 1) Since this is all individual, the turnover of individuals makes it harder. Right now I've got about 4000 wrestlers in the database. How many are graduating? But I'd have to keep them around in order for this to be useful. So next season comes and we are talking....6000 maybe? That's huge. And then 24000 matches? Already on my office computer (the fast one) this takes more than 12 hours. On my laptop, it's more than 24 hrs. I need to figure out how to port this onto the supercomputer (I don't know if it has a microsoft license) 2) Even if they don't graduate, how many of the change weight classes. Another challenge. But this question is outside of the bigger issue - can there be an easier way to get data? I just don't think importing from track like I'm doing now will be sustainable. We'll see. Anyone here from InterMat or Flo or WrestleStat who wants to collaborate? I'll give you rights to publish if you can provide a more convenient access to scores.

The biggest upsets (remember Hendrickson over Steveson is 880) 5 149:North Dakota St_Gavin Drexler over 149:Illinois_Kannon Webster difference=1526 (15.4% chance; 22 seed over 7) 4 165:Central Michigan_Chandler Amaker over 165:Bucknell_Noah Mulvaney difference 1595 (14.4% chance; 33 seed over 17) 3 174:Army_Dalton Harkins over 174:Nebraska_Lenny Pinto difference=1774 (11.8% chance; 25 seed over 2 165:Hofstra_Kyle Mosher pinned 165:Arizona St_Nicco Ruiz difference=1825 (11.2% chance; 16 seed over 15; Pablo did NOT like Mosher) 1 197:Rider_Brock Zurawski over 197:Northern Iowa_Wyatt Voelker difference = 1957 (9.6% chance; 26 seed over 7)

Sorry, I've been out of town, but I've now taken a quick look at the results for Pablo for the NCAA tournament. Looking at all the wrestlers in all the matches, excluding the medical forfeits and I also culled out the DQ at 285 (not necessarily justified, but it won't matter because it doesn't affect the comparison), the higher ranked wrestler in Pablo won 74.4% of the time. No one who was rated more than 2000 higher than their opponent lost. For context, let's compare it to seeds. By my math, I have the higher seed winning 468 and losing 168, for an overall success rate of .... 73.6%. A little behind, but not huge, for sure. It's a difference of 5 matches. I won't claim that it is statistically significant or anything, but for sure, there is nothing here to say that seeding is any better. I'm calling that a success for Pablo. One thing I do want to comment on: Steveson vs Hendrickson. I haven't gone back to the thread where we discussed probabilities, but recall my post where I talked about my interpretation of them. In particular, where I talked about how I use 15% as the line of "it wouldn't a surprise." Now, I don't remember for sure, but I think I actually talked about 285 in that context, and said something like "Certainly, we would expect Steveson to win, but it wouldn't be a surprise if Hendrickson pulled it off" (recall Pablo gave Hendrickson a 15.8% chance of winning the title, and, if you look it up, it was a 28% chance of beating Steveson). So for all the people insisting that this was the "biggest upset" in championship history and that this was a total shocker, the answer is no. Pablo told you all that this was possible, and wouldn't actually be a surprise. I know it's always a surprise to see a #1 seed lose, and yeah, Steveson is an olympian, but when you look at what they've done this season, Hendrickson has been legit. Pablo was telling us ahead of time that they were closer than people were crediting. It was an exciting match, for sure, but the outcome didn't surprise me.

If it's "harder to achieve" then, yeah, I think that's better. Holding someone on their back for 4 seconds is harder than holding someone for 2 seconds, so is worth more points. Why shouldn't it be the case that doing things that are harder to do is worth more? You and I have very different ideas of what wrestling is about. I view the objective of wrestling to be a) take your opponent to the mat b) expose their shoulders to the mat c) pin those shoulders to the mat. You, apparently, are content with (a). Yes, points are about incentives. Points in folkstyle reward activities that lead to the objective of pinning your opponent. Takedowns do that, for sure. But so does a reversal. Takedowns can be exciting (although watch Bo Bassett wrestle - takedowns are boring), but back points are more exciting. And reversals to the back are crazy exciting. I want to see wrestlers trying to pin their opponents. You, apparently, don't.

To be fair, that would be my strategy against Steveson, but I don't think I'd get to the 2nd round of the NCAAs You'd think by the time you've been given the 2 point penalty you'd change your approach.

I don't want to dig through the thread (even if it's here) what was the nature of the DQ for Slavikouski?

I don't get the appeal of freestyle. I want to see wrestlers trying to pin their opponent's back to the mat, not grab them by their knees and roll around the floor. I do get, that at the highest levels, freestyle makes sense. These guys are just too good and strong to be able to hold them on the mat long enough to turn them over. Consequently, the folkstyle match consists of each wrestler choosing down to get one point for an escape when it's their turn, and the rest of the points come from either takedowns or stalling. At that point, you might as well be wrestling freestyle. And hey, to make it more exciting, give you a near fall if you just expose their back to the mat. But while it's true that the wrestlers are too good for folkstyle at the highest levels, it's not at all the case for 99.99% of wrestling matches below D1, and not the case for 100% of the women at all levels. /soapbox

Make a reversal worth 3. I don't understand why a reversal should not get the same as a TD. Better yet, make it 4. Make it equivalent to an esc + TD. Call me crazy, but I think a reversal is a better wrestling move than a TD. Better in that, it's harder to accomplish and therefore should be worth more. It bothers me that a TD+esc is worth twice as much as just going straight from the defensive position to offense.

I should add, the #1 wrestler at 125 is the most likely to be "the first" to lose if it happens in the quarters

Pablo had Lilledahl as a 70% favorite over Seymour. That means Seymour had a 30% chance to win. In my world of "anything with a 15% is not surprising," I can see it was not expected, but not a surprise. Pablo had Seymour with a 14% chance of making the semis, but once he got through Smith, that jumped to 30%. 125 is a very competitive weight class. Bummer that Ramos went down, too.

I was officiating a dual last year, and with one team, when I'd blow the whistle, the coach would start counting down, "10...9...8..." I never talked to him about it, but I got the idea that he was challenging his guys that they had to make an aggressive wrestling move within 10 seconds. They certainly were aggressive, and a lot of fun to officiate, because there wasn't any stalling.

To follow up on that comment, one thing I've learned over the years of doing this is that it doesn't do much good to get hung up on individual outcomes where Pablo was right or Pablo was wrong. The NCAA tournament is like 650 matches. There are going to be places where seeds get it right and Pablo gets it wrong, places where Pablo gets it right and seeds get it wrong, and times when both are right or both are wrong. Remember, the Fundamental Theorem of Pablo is that upsets happen. They must happen, so Pablo must get matches wrong. Similarly, seeds will get some matches wrong. In the end, we'll see. So again, patience. Just enjoy the action. Go Panthers. Go Boilers. Go everyone who is wrestling against an Iowa guy.

If you will be patient, I will do the analysis after the tournament.

The question came up the other day about bonus points, so I've taken a look at how we can predict bonus points based on Pablo ratings. Using the predictions for conference tournaments, here is the distribution of outcomes for wins as a function of Pablo rating differences So when the rating difference is below about 200, something like 84% of the matches won by the favorite are decided by a regular decision (when the rating difference is that low, the breakdown of losses is pretty similar). 9% are falls, 5% MDs and the very rare TF. But when you get to other end, where the difference is greater than 3000, then it's only 14% regular decision, with something like 37% MD, 33% TF and 15% falls. There is a pretty steady increase in the rate of MDs and TFs as you increase the rating difference, but there looks like there is a step of MD going to TF at about 2000. Although there is a little bit of an increase in the pin rate, it's really not all that much, consistent with the idea that pins are not completely about difference in quality. Yeah, when the difference is larger, it's more likely to be a pin, but not by much (maybe 50% greater than when they are even), unlike MDs and (especially) TFs which increase big time. From these data, we can talk about expected returns in terms of bonus points. If we take a dual approach, scoring 3,4,5,6, then we can talk about the average expected points for a ratings difference. For example, if the difference is around 150, the average value of a win is 3.3 points. List below 150 3.3 425 3.4 715 3.5 1125 3.6 1485 3.8 1945 3.9 2515 4.2 3300 4.5 So by the time the rating difference gets to 2000, then the average match outcome is a MD (and scoring-wise, a MD is a tad better than a fall (because falls are not strongly correlated with ratings difference)). Out in the 3500 range, the average outcome is somewhere between a MD and TF. What about tournament activity points? We can do that as well. Using 1, 1.5, 2 as the bonus points, we have 150 .25 425 .34 715 .40 1125 .51 1485 .64 1945 .70 2515 .97 3300 1.18 Same conclusions, but different values. The lesson here is, if you use Pablo ratings to predict points, assume chalk and count matches and advancements, you can add on the expected bonus points to get a more refined estimate.

Nah, they do blood cleanup and stuff, but none of them will say, "You got 5 minutes to stop the bleeding or you're done" Cumulatively.

How many other sports have Blood Time?

Did you use the ratings then ("chalk") to get the places based on outcomes through the consolation brackets? Or did you just call them 1 - 8? The biggest worries I would have about this approach is 1) the lack of advancement points for non-place winners, and 2) matchup effects due to differences in seeding. But it's a great start. In particular, "assuming everything is chalk according to Pablo" is the best approach. Because while the race is not always to the swiftest, nor the fight to the strongest, that's the way to bet. If I were to do this, of course, I'd make it a lot more complicated and do a bunch of simulated tournaments to see what kind of ranges those scores can adopt and the likelihood of each. ETA: I've also been thinking if there is a way to estimate the likelihood of bonus points given a ratings difference.

I really don't know how to do that. I get the idea that you can use some estimates based on final placements, but I really don't even have that. I can absolutely use advancement through the championship bracket, and it would be trivial to add in a probability for finishing 2nd, but I am not up for going into the consolation bracket to score either advancement or even places. That would take a simulation effort that I don't want to get into. If you look at the video I linked the other day, you can see that I've done these type of big-ass simulations, but it's a lot of effort. At some point in my life, if I can keep this going, this is something that I could do (although if you ask, I could give you the information you could use to do it yourself! Not much more is needed; if anyone wants to try it, DM me).