But perhaps the largest storyline of the tournament so far has been the Big 10. With 9 teams originally qualifying, per 538’s data, the Big 10 had a 0.158% chance of not having any teams reach the Sweet 16. Just prior to the Michigan game, that same probability had risen to 25.6% with 8 Big 10 teams eliminated. Michigan is now the only Big 10 team left standing.

#1 Illinois vs. #8 Loyola Chicago

The first major upset of the Second Round happened to happen in the first game, with Loyola Chicago a 13-point loss. Going into the game, the probability model I use gave Illinois a 64.66% chance of winning, with an estimated point margin of 4.14. With the loss, Illinois became the 21^st 1 seed to not make the Sweet 16. Whatever questions existed about how Loyola Chicago’s defense would hold up against more athletic teams than they typically see in the Missouri Valley Conference have surely been erased, as Illinois was held to under 60 points for the first time this season. Loyola’s defense held Illinois to 28.6% 3pt. shooting (9% below Illinois’s season average) and created 17 turnovers (4 more than Illinois’s season average). All this was done while giving 13 total free throws to Illinois, compared to the 22 total Loyola Chicago received.

With the result, Loyola Chicago and Illinois metrics-based chances of making the Final Four essentially flipped. Loyola Chicago’s chances soared from 10.69% to 30.27%: the largest total gain in the tournament up until this point. Unsurprisingly, Houston’s chances of making the Final Four went up ~5% as well, largely due to the best metrics-based team in the region now out of contention.

Interestingly, this switch reflects a trend I first learned about in this video from Secret Base. Before the tournament kicked off, I looked at the calculated average win percentage by seed in the Sweet 16. The 6-10 seeds all have an easier path if they manage to get past the winner of the 1 vs. 16 game in the Second Round. This has certainly held true for Loyola Chicago, whose chances of reaching the Final 4/winning the National Championship originally faced a roadblock in Illinois.

This is certainly applicable in Loyola Chicago’s case, who now faces 12 seed Oregon State in their next matchup. The same model gives Loyola Chicago a 73.5% chance of winning, with an estimated point margin of 7.

#3 West Virginia vs. #11 Syracuse

The Midwest Region led the other regions in total number of upsets during the Round of 32 (with 3), and the second of the day happened when Syracuse beat West Virginia 75-72. This caused a similar pattern that occurred with the Loyola Chicago game to occur, except with Syracuse overtakes West Virginia’s previous chances of reaching the Final 4 after the results of this game were factored in.

#7 Florida vs. #15 Oral Roberts

I am pretty sure it is safe to say that Oral Roberts is the biggest Cinderella story in a tournament with many Cinderellas. Coming off their upset win over #2 Ohio State, they beat #7 Florida 81-78, improving their chances at winning the championship another 487% over where they were prior to the Round of 32. Their chances of winning have now gone from 0.000168% at the start of the tournament to 0.00644% at the end of the second round. For a visual representation of just how much the Florida win impacted their chances compared to the first round, check out this graph:

#4 Oklahoma St. vs. #12 Oregon St.

If anything, the PAC-12 has surprised many in just how underrated some of its teams are, while the Big 10 and Big 12—despite the number of teams admitted from each conference—have been largely underwhelming. This matchup between Oklahoma State and Oregon State was another example of this. I will point out that Oklahoma State sits at 33^rd in the KenPom ratings at time of writing, with Oregon State not sitting too far behind at 48^th, so the seed differential of 8 between the two teams is a bit deceiving. Nonetheless, the PAC-12 tournament champions Oregon State came out with a 10-point win over the Big 12 runner-up. Oregon State held Oklahoma State to under 30% shooting (both overall and 3pt), 19% below their season average FG% and 7% below their season average 3PT%. With potential tough matchups against Loyola Chicago and the winner of Syracuse/Houston facing them, the Beavers sit with about a 6% chance of making the Final 4, up 4.3% total since the start of the round.

And that’s just Day One.

#2 Iowa vs. #7 Oregon

This one shocked a lot of people. In what was essentially an offensive shootout, Oregon took control of the game toward the end of the first half and never looked back in yet another example of the PAC-12 outperforming projections while the Big 10 and Big 12 underperform (I’ll be doing a deeper analysis of this at some point, I’m sure). Oregon was a pretty sizable underdog coming into this game, and the 8% total jump in their chances of making the Final 4 shows that (about a 261% jump).

I think the most interesting thing about this game wasn’t that Iowa necessarily played badly—they pretty much were a touch below their season averages in most statistical categories. Rather Oregon’s offense was able to catch fire, allowing Oregon to put up 95 points (20 above their season average, and with ~10% better than average FG%) and the Iowa defense was unable to stop it.

#3 Kansas vs. #6 USC

A lot of people had doubts about USC and their potential to make a deep run. USC seems to just keep proving those doubts wrong, putting up a 34-point win over the #3 seed. In yet another example of the PAC-12 beating lower-seeded Big 10 and Big 12 teams, USC managed to hold Kansas to their lowest score of the season, and their third worst loss in program history. USC’s chances of making the Final 4 nearly doubled as a result of the game, up to 17.38% from 9.48%. Metrics-wise, USC was actually favored to win the game by 1.5 points, translating to a win probability of 55% favoring USC.

Notes on Methodology:

These probabilities all assume that metrics in this pandemic season are a measure of ability. They are and they, as we’ve seen, are not. It’s all part of why we watch and what leads to the Madness of March. 

To calculate win probability for an individual match, KenPom’s Adjusted Efficiency Margin and ESPN’s BPI metrics are first used to determine an estimated point margin. Factors such as notable injuries and rest days are taken into account when generating the point margin. This point margin is then applied to a normalized distribution curve with a standard deviation of 11 (what KenPom uses to calculate its win probabilities). To come up with a team’s odds of winning the tournament, weighted averages of win probabilities against all possible teams in each round of the tournament are used. This yields similar results to FiveThirtyEight and other groups predicting tournament/round win probabilities.

The post Round 2: The Madness Continues first appeared on 'CATS STATS.

2^12 yields that for 12 games, there are 4096 possible outcomes. Dividing 14.7 million by 4096 reveals that each outcome should be present ~3589 times, or about 3.45x as many perfect brackets as there were at that point in time. This trend continued to hold for a few games, before human perfect brackets started to outperform the coin toss again. By end of the first day of games, 14.7-million coin-flip brackets would have ~224 that remained perfect, compared to the 108 remaining. When the last brackets were finally busted late on the second day, human-picked brackets had once again become more effective.

Tournament Win Probability Swings:

Through the first 32 games, teams’ chances of winning the tournament have fluctuated quite a bit. Here’s an overview of the largest gains as a result of individual games, both by percent change and total increase:

By % Change:

By Total Increase:

If looking at the largest swings over the entire course of all 32 games:

By % Change:

By Total Change:

If the percent change for each team is graphed, it forms a pretty nice pattern that makes it easy to see where major upsets occurred, along with how impactful an upset was for the rest of the field.

Overall, two games in the first round stand out as particularly impactful even for teams that did not play in them: #13 Ohio’s upset over #4 Virginia and #2 Oral Roberts upset over #2 Ohio State. In the case of the Ohio upset, Creighton (their opponent in the next game) will now no longer have to take on a Virginia team with better metrics (via KenPom and ESPN BPI) than Ohio. Their chances of winning the entire tournament increased .47% total as a result.

Further, Gonzaga, after their chances decreased throughout the day due to a lack of upsets in their region, went up 1.36% total again due to Virginia’s exiting the tournament.

And in the case of the Oral Roberts upset, Florida’s chances changed significantly, up 97%, due to now facing Oral Roberts rather than an Ohio State team with significantly better metrics.

These probabilities all assume that metrics in this pandemic season are a measure of ability. They are and they, as we’ve seen, are not. It’s all part of why we watch and what leads to the Madness of March.

Notes on Methodology:

To calculate win probability for an individual match, KenPom’s Adjusted Efficiency Margin and ESPN’s BPI metrics are first used to determine an estimated point margin. Factors such as notable injuries and rest days are taken into account when generating the point margin. This point margin is then applied to a normalized distribution curve with a standard deviation of 11 (what KenPom uses to calculate its win probabilities). To come up with a team’s odds of winning the tournament, weighted averages of win probabilities against all possible teams in each round of the tournament are used. This yields similar results to FiveThirtyEight and other groups predicting tournament/round win probabilities.

The post Round One Recap first appeared on 'CATS STATS.