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Stat Corner Powered By WrestleStat: One-Point Matches

Stat Corner

A common refrain from wrestling fans as a competitor wins close match after close match is, “If you keep wrestling like that, you’re going to lose one eventually.”  This conventional wisdom is correct.  Last year in Stat Corner powered by WrestleStat, we detailed how winning one point matches is not a sustainable strategy.  Those teams and wrestlers that are winning a high percentage of one point decisions will revert to the mean at some point.  The same is true for those losing a high percentage of the closest matches.  Everyone will converge on a .500 record over the long haul.  This makes it useful to know which teams are currently sitting on either end of the bell curve.

The top-20 teams by winning percentage in one point matches are:

Rank School Matches Wins Win %
1 North Carolina 36 32 0.889
2 Ohio State 9 8 0.889
3 Iowa 16 13 0.813
4 Wisconsin 20 15 0.750
5 Virginia Tech 21 15 0.714
6 Sacred Heart 10 7 0.700
7 Arizona State 23 16 0.696
8 Navy 35 24 0.686
9 Southern Illinois Edwardsville 21 14 0.667
10 Central Michigan 30 20 0.667
11 Duke 23 15 0.652
12 Gardner-Webb 20 13 0.650
13 Eastern Michigan 17 11 0.647
14 South Dakota State 14 9 0.643
15 Stanford 30 19 0.633
16 Virginia 24 15 0.625
17 Cal Poly 13 8 0.615
18 Rider 31 19 0.613
19 Pennsylvania 10 6 0.600
20 Utah Valley 27 16 0.593

The sample size is important here so drawing too many conclusions from Ohio State’s nine one point matches would be unwise.  However, North Carolina’s 32-4 mark is ripe for a correction.  Iowa, Wisconsin, and Virginia Tech are somewhere in between.  Notice how few teams manage to win more than 70% of these matches.  Not even all of the top-20 exceed 60%.  Back to the Tar Heels, there is no guarantee that they’re suddenly going to start losing close ones.  However, only three teams finished above .700 last season and none won more than 78.3% of one-point matches.  Only one team topped .700 in 2015-16 as well and no team has outdone the .782 Brown posted in 2016-17 in the previous four years.  There will be a regression, but whether it comes over the next six weeks or not until next year is anyone’s guess.

Rank School Matches Wins Win %
1 The Citadel 15 3 0.200
2 VMI 10 2 0.200
3 Michigan State 19 6 0.316
4 Harvard 21 7 0.333
5 Bloomsburg 29 10 0.345
6 Northern Colorado 19 7 0.368
7 Northwestern 24 9 0.375
8 Oregon State 16 6 0.375
9 Lock Haven 29 11 0.379
10 Army 28 11 0.393
11 Northern Illinois 20 8 0.400
12 Hofstra 25 10 0.400
13 Oklahoma 47 19 0.404
14 Iowa State 24 10 0.417
15 Binghamton 14 6 0.429
16 Oklahoma State 23 10 0.435
17 Bucknell 25 11 0.440
18 Illinois 20 9 0.450
19 Davidson 20 9 0.450
20 Buffalo 22 10 0.455

So, which teams can expect to, at some stage, start catching a few more breaks?  No one has finished under 25% since 2013-14 and going a full-season under 30% is rare.  That is good news for The Citadel and VMI.  Even teams like Northwestern and Lock Haven can expect to move closer to .500 in the long term, though their cold streak could last through March.  Again here we see very few teams truly sitting at the extremes.  There is a slight skew of Division I starters winning more than 50% of one-point matches, suggesting there could be a small talent aspect involved, but we see from year-to-year, teams always head back to the middle.

Rank School Matches Wins 2017 2018 Diff
1 North Carolina 36 32 0.463 0.889 0.425
2 Sacred Heart 10 7 0.364 0.700 0.336
3 Ohio State 9 8 0.571 0.889 0.317
4 Iowa 16 13 0.538 0.813 0.274
5 Utah Valley 27 16 0.333 0.593 0.259
6 Duke 23 15 0.421 0.652 0.231
7 Navy 35 24 0.462 0.686 0.224
8 Cal Poly 13 8 0.393 0.615 0.223
9 Wisconsin 20 15 0.541 0.750 0.209
10 West Virginia 31 15 0.303 0.484 0.181

This table highlights that point.  North Carolina won just 46.3% of their one-point matches last season before their torrid streak in 2017-18.  Throughout this table of the 10 teams with the greatest positive difference in winning percentage from last year to this one, we see a group of teams either jumping back up to the middle ground after a bad season or taking off from the middle to spend some time at the extreme.  They will likely revert in 2018-19.

Rank School Matches Wins 2017 2018 Diff
1 The Citadel 15 3 0.519 0.200 -0.319
2 Oklahoma State 23 10 0.703 0.435 -0.268
3 Brown 33 17 0.783 0.515 -0.267
4 Penn State 14 7 0.762 0.500 -0.262
5 Oregon State 16 6 0.613 0.375 -0.238
6 Buffalo 22 10 0.667 0.455 -0.212
7 Oklahoma 47 19 0.611 0.404 -0.207
8 Lock Haven 29 11 0.560 0.379 -0.181
9 Northern Iowa 26 13 0.679 0.500 -0.179
10 Harvard 21 7 0.500 0.333 -0.167

This is the inverse of the table just above it with those teams suffering the greatest negative change from last year to this one.  Each of the three teams that managed to win over 70% of their one-point matches last season make the top four with none of them doing better than .515 this season.  Brown’s record-setting 2016-17 hasn’t kept them from being average during this campaign.  Oklahoma State has had a bit of a disappointing year so far.  Some of that might be explained by dropping from one of the best teams in the closest matches to one that is subpar.  Everyone will jump to blame Dean Heil and he is 0-2 in one-point matches, but the rest of the squad is just 10-11 themselves.

The question many of you will ask is, what do we do with this information?  That is a tough question when you realize how few one-point decisions each individual wrestler is involved in over their career.  The sample size just doesn’t get big enough for most for the luck factor to even out.  However, teams, especially over multiple years, get enough that it always balances out.  We may not be able to pinpoint which wrestler will fall victim when the regression hits, but we know it will happen.  Looking at North Carolina’s prospects heading into the post-season and next year, we should probably temper expectations just a bit.  Teams such as Northwestern and Lock Haven might deserve a bump as they are likely to get a few extra victories.  More important than predicting future performances, I believe it is important to acknowledge that there is some chance involved in who wins the closest matches.  We know this, anecdotally, given the number of judgment calls, odd occurrences, and variance in athletic performance involved.  However, seeing how predictable the regression is when the sample size rises, we now have proof that, in the short term, a team can be lucky or unlucky.  Eventually, talent and ability will be the determining factor, but if the cold streak hits at the wrong time, the effects could be a big problem.

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