Dominate Your Office Pool: An In-Depth Guide to NCAA Bracket Simulators and Strategic Predictions

Every year, the NCAA Men's Basketball Tournament captivates fans with its thrilling upsets and unpredictable outcomes. As tip-off approaches, millions participate in bracket pools, attempting to predict the tournament's winners. While luck plays a role, understanding the underlying principles and utilizing data-driven strategies can significantly improve your chances of success.

Understanding the NCAA Tournament Bracket

The NCAA Tournament bracket is a single-elimination tournament featuring 68 college basketball teams in the Division I. Of the 68 teams, 32 earn automatic bids by winning their respective conference championship tournaments, while the remaining 36 receive at-large bids based on their regular-season performance, as selected by the NCAA Selection Committee. The committee is responsible for selecting, seeding, and bracketing the field.

These teams are then seeded from 1 to 16 within four regions: East, West, Midwest, and South. The highest-seeded teams play against the lowest-seeded teams in each region (e.g., the No. 1 seed plays the No. 16 seed). The tournament progresses with winners advancing to the next round until a champion is crowned.

The "First Four" refers to the four play-in games that occur before the main tournament. In these games, eight teams compete for the final four spots in the 64-team bracket. While these teams are often considered long shots, they have occasionally made deep tournament runs, such as VCU in 2011 and UCLA in 2021.

The Allure of March Madness

March Madness is fun and free, and you can fill one out quickly, depending on how seriously you take it. Like one mascot over the other? Recognize one of the schools? Any strategy can work. Because of the nature of such a large single-elimination tournament, every game matters. You’ll root for outcomes that are favorable to your bracket. This is one reason March Madness is such a thrilling event - you can quickly become a fan of the teams you’ve picked, even if you know next to nothing about them. Not to mention, every game is fast-paced and jam-packed with action. Even if you’re not a sports fan, March is a chance to feel like one.

The Power of Bracket Simulators

NCAA bracket simulators leverage data and statistical analysis to project the outcomes of tournament games. These simulators often incorporate factors such as team efficiency, historical performance, and point spreads to generate probabilities for each matchup.

As tip-off to the NCAA Basketball Tournament approaches, serious and casual fans of the sport have likely already made at least one attempt to fill out a bracket for their office pools. Before you make that final submission, I have a bit of advice for you.

NCAA Tournament results can be predicted to some extent based on point spreads, and point spreads can be estimated using efficiency data, such as the values published by Kenpom. If we combine this knowledge with the baseline information of how a typical tournament usually plays out, it gives us a chance to identify where the most likely upsets will happen.

Key Metrics Used in Simulations

Kenpom Efficiency Margin: This metric, developed by Ken Pomeroy, measures a team's adjusted efficiency margin, calculated as the difference between its adjusted offensive and defensive efficiency. It provides a comprehensive assessment of a team's overall performance.
Monte Carlo Simulations: These simulations run thousands of iterations of the tournament, using Kenpom efficiencies to project point spreads and single-game odds. The results provide probabilities for each team to advance to each round.
Historical Averages: Comparing a team's performance to the historical average for its seed provides context and helps identify teams that are overperforming or underperforming relative to expectations.

Analyzing the Regions: A Data-Driven Approach

To effectively utilize bracket simulators, it's crucial to analyze each region and identify potential strengths, weaknesses, and dark horse candidates. Here's a breakdown of how to approach this analysis:

West Region

Figure 1 compares the Kenpom adjusted efficiency margin for the 18 teams in the West relative to the historical average Kenpom efficiency of teams with the same seed. This tool is very useful to quickly see which seeds in the region are weak or strong, relative to historical averages.

Read also: Crafting Your NCAA Profile

At a first glance, a few key things stick out from Figure 1. First, Gonzaga is really, really good, and Iowa is also a very strong No. 2 seed relative to historical averages. As for the rest of the region, USC stands out as a strong No. 6 seed and No. 15 Grand Canyon has a very strong efficiency margin for a team seeded that low. It likely won’t matter, but that is interesting.

Table 1 gives the Monte Carlo simulation results for the 18 teams in the West Region.

This table contains a ton of information. In the left-most section is the current Kenpom efficiency margin for each team. Next to that column is the Kenpom efficiency margin relative to the historical average efficiency margin for teams with that seed. This is essentially the same data plotted in Figure 1.

For example, Gonzaga is actually entering this tournament with the best efficiency margin on record since 2002 when reliable Kenpom data is available. The Zags’ margin of +38.05 is 9.31 larger than the historical average efficiency margin of all previous No. 1 seeds.

My simulation predicts that the Gonzaga Bulldogs have an 80 percent chance to reach the regional final, a 63 percent chance to reach the Final Four, and a 37 percent chance to win the National Championship. Note that these odds are the best of any team on record and slightly better than the odds for the 2015 Kentucky team (35 percent) who held the previous record.

That said, these numbers are somewhat meaningless without context. That is where the final section of the table comes in on the far right. These are the odds for each team to advance to each round relative to the odds for an average team of that seed in a tournament filled with other historically average teams.

For example, according to Kenpom, the Iowa Hawkeyes are a strong No. 2 seed. As a result, Iowa’s odds to make the Sweet 16 are 8.5 percent better than an average No. 2 seed and their odds to make the regional final are 11.5 percent better than average. However, due to the presence of Gonzaga in the half of the region, the Hawkeyes have a below average (by two percent) chance to make the Final Four.

Note that I have also sorted the teams in Table 1 according to their odd to win the region (i.e. advance to the Final Four). Already, we can start to see the main dark horse team emerge: USC. Despite being just the No. 6 seed and having only the fourth best Kenpom efficiency in the region, the Trojans have the third best odds to make it to the final weekend.

South Region

As Figure 2 and Table 2 show, almost every team in the South Region, with the exception of No. 12 seed Winthrop, is above average for their seed. A few teams, such as No. 5 seed Villanova, No. 6 Texas Tech, No. 11 Utah State, No. 13 North Texas, No. 14 Colgate, and especially No. 8 and No. 9 seeds North Carolina and Wisconsin are noticeably above average.

On balance, the South looks like a strong region, which is depressing Baylor’s odds to make it to the Final Four. In particular, Baylor’s second round game against the winner of the Wisconsin/North Carolina game could be a potential upset. In addition, on paper, Villanova looks to be the most likely dark horse in this region.

That said, this is the perfect time to mention the obvious caveat to this analysis and methodology. Kenpom efficiencies are simple averages of a team’s performance over the entire year. In the case of Villanova, they had a key injury late in the season when Collin Gillespie torn his MCL, and the Wildcats have not played well since. As a result, we cannot trust the numbers associated with Villanova in Table 2, in my opinion.

Midwest Region

According to Figure 3 and Table 3, the teams that look to be more dangerous than usual are No. 8 Loyola, No. 9 Georgia Tech, No. 2 Houston, No. 6 San Diego State, and No. 16 seed Drexel.

On the flip side, this bracket also seems to have a few potential weak links in No. 3 West Virginia and No. 4 Oklahoma State. Also, No. 12 Oregon State and No. 14 Morehead State are below average for their seeds as well.

In general, this looks like a sneaky tough road for the Fighting Illini. The boys from Champaign are an above average No. 1 seed, but the odds for them to make the Final Four are barely above average. In the first round, the Illini draw an above average No. 16 seed in Drexel, and after that, they have to face the winner of the No. 8 No. 9 seed game between Loyola and Georgia Tech, who are both well above average for their seed.

If the Illini survives until the regional final, it is more likely that they will meet No. 2 seed Houston who Kenpom currently estimates as the strongest of the No. 2 seeds and the most likely one to win the National Title. In addition, No. 5 seed Tennessee also looks to be above average and a possible dark horse.

In a normal year when all teams have played a full non-conference schedule, I think that the analysis above would be pretty solid. However, I do have some doubts as to whether mid-major teams like Houston and Loyola are actually as good as their current Kenpom averages suggest. If they are, Illinois might be in some trouble.

East Region

Similar to the South Region, most of the teams in the East look to be above average, relative to the historical values. The main exception is No. 3 seed Texas.

As shown in Figure 4, it is the teams in the middle of the seed list, notably UCONN, Saint Bonaventure, LSU, and Maryland, along with No. 14 seed Abilene Christian, who are prime candidates to cause trouble.

Looking at the Final Four odds in this region, Michigan has the best odds on paper, which are actually better than an average No. 1 seed. However, with the injury to Isaiah Livers and another potentially tough matchup in the second and third rounds, I have my doubts that the Wolverines will live up to expectations.

Furthermore, No. 2 seed Alabama and No. 3 Texas also look to have lower than usual odds to survive the region. In total, this tells me that the East Region is the most likely one to descend into Madness, resulting in a team seeded lower than No. 3 making it to the Final Four. Right now, I like Florida State’s odds.

As for Michigan State’s chances, their odds to advance are clearly depressed by the fact that the Spartans have to play an extra game in the play-in round (which I do not explicitly handle in the comparison to historical averages). That said, I project that MSU has only a 14 percent chance to make it past BYU, only a five percent chance to make it to the Sweet 16 and less than a one percent chance for a magical run to the Final Four.

That said, these simulations are assuming that Michigan State is only as good as their Kenpom averages suggest. Michigan State has seen long odds before this season, and made the tournament anyway. If MSU can play close to their ceiling of potential, I do believe that the Spartans can make a run. But, the numbers do not currently support that idea.

First-Round Upset Picks: Identifying Opportunities

Identifying potential first-round upsets is crucial for differentiating your bracket and maximizing your scoring potential.

From Figure 5, we can clearly see which upsets in each pairing are more likely than average (the ones below the blue line) and the ones that are less likely (the ones above the line).

Starting with the top seeds, as expected there are no clear first round upset picks for the No. 1 and No. 2 seeds. For the No. 3 seed, however, things get interesting, as both Texas and Arkansas have been paired with stronger than usual No. 14 seeds in Abilene Christian and Colgate.

For the interest of full disclosure, I should say that I have done some math, which suggests that it is never a good idea to pick a team seeded No. 3 or higher to get upset in the first round. After all, the odds for Texas to win are still 75 percent. In the case of Colgate, the Raiders played such an abbreviated schedule that I simply don’t trust the validity of their Kenpom efficiency anyway.

But Abilene Christian over Texas? I am very, very tempted to make that pick, especially since my analysis suggests that Texas would likely lose to No. 6 BYU in the next round anyway (if they make it that far). If you are feeling bold and want a good “big” first round upset, that is the one that this analysis suggests.

As for the No. 4 seeds, there is no clear upset recommendation on this line this year. Purdue and Oklahoma State have slightly lower odds than usual, but not by much. If I were to make a recommendation, however, this is a case where the intangibles might play a leading role. Virginia had to back out of the ACC Tournament due to COVID issues and their roster status seems to be in question. If a No. 13 seed is going to win this year, my pick is for Ohio to beat the Cavaliers.

As for the No. 5 seeds, this frankly looks like a bad year for the famous No. 5/No. 12 upset. The No. 12 seeds all look weak this year and the No. 5 seeds are relatively strong. It might make sense to pick Villanova to lose without Gillespie, but Winthrop is a very weak No. 12 seed. My pick is for Big East Tournament Champs No. 12 Georgetown to upset No. 5 Colorado. The current Vegas lines also support this pick as the most likely.

There are also no clear upset picks on the No. 6 seed line. USC looks safe, but the other three teams could run into trouble. Of course, the best outcome for Spartans fans would be for MSU to knock off BYU after defeating UCLA. That projects to be the second most likely upset on this line, second only to Utah State over Texas Tech. History tells us that one or two of these games is a likely upset.

On the No. 7 seed line, there is a very clear upset recommendation: No. 10 Rutgers to take out No. 7 Clemson. The current Vegas line also has the Scarlet Knights favored, so this is perhaps the best upset pick of the entire first round. As for the other games, Kenpom has the Oregon/VCU game as the next most likely upset pick, but the Vegas line suggests otherwise. The other two games do not inspire me to select an upset winner, but it would not shock me to see Maryland beat UCONN.

Finally, the No. 8/No. 9 games are always a coin toss and the Kenpom data and Vegas lines are just causing confusion. Based on Figure 5 (from Kenpom) taking Wisconsin and Saint Bonaventure as “upset winners” makes the most sense. But, the Vegas lines favor the No. 8 seeds across the board, so this is a tough call. Personally, I like Wisconsin and Georgia Tech to win as No. 9 seeds, but that is more of a gut feeling.

Recommended First-Round Upsets

No. 14 Abilene Christian over No. 3 Texas
No. 13 Ohio over No. 4 Virginia
No. 12 Georgetown over No. 5 Colorado
No. 11 Utah State over No. 6 Texas Tech
No. 11 MSU over No. 6 BYU (total homer pick)
No. 10 Rutgers over No. 7 Clemson
No. 9 Wisconsin over No. 8 UNC
No. 9 Georgia Tech over No. 8 Loyola-Chicago

As I demonstrated in part one of this series, eight total first round upsets is exactly on the historical average.

Strategic Bracket Filling: Beyond the First Round

While first-round upsets are important, success in bracket pools hinges on accurately predicting the later rounds. Consider these strategies:

Weighting Higher Seeds: The higher-seeded a team is (No. 1 seeds are the highest), the more likely that team is to win.
Identifying Potential Final Four Teams: Focus on teams with strong Kenpom efficiencies and favorable matchups.
Accounting for Injuries and Momentum: Consider injuries to key players and recent team performance, as these factors can significantly impact a team's chances.
Understanding Bracket Scoring: Bracket pools award points for each correct pick, usually increasing the points for each round. So choosing the winner of a first-round game may be worth two points, then in the second round, correct picks are worth four points each, and so on. The more correct picks you have late in the tournament, the more points you get.

Advanced Analytics: Delving Deeper

For those seeking a more in-depth understanding of college basketball analytics, consider these concepts:

Points per Possession (PPP): This metric measures a team's efficiency on offense and defense, calculated as the number of points scored or allowed per 100 possessions.
Four Factors of Offensive Success: Dean Oliver identified four key factors that contribute to offensive efficiency: shooting (effective field goal percentage), offensive rebounding, turnovers, and getting to the foul line (free throw rate).
Strength of Schedule Adjustment: Adjusting team statistics for the strength of their opponents provides a more accurate comparison of team performance.

The Four Factors

Shooting is the most important of the four factors, not any kind of surprise.
Offensive rebounding and turnovers have about the same importance but less than shooting.
The least important factor is getting to the foul line.

College Basketball Analytics

To estimate the number of possessions in the game from the box score, let’s consider how a possession can end. One way for a possession to end is a turnover (TO). Next, consider possessions with a field goal attempt (FGA). The possession could get extended if the offense grabs the rebound (OREB). To account for these 3 situations, we estimate possessions with a field goal attempt by FGA - OREB. A possessions can also end with free throws. If every free throw came in a pair because of a shooting foul, the number of possessions would be half the free throw attempts (FTA). Instead of a half, you need a different factor that accounts for these situations. In college basketball, teams could differ in the number of possessions by two if one team gets an extra possession each half. To estimate the possessions in one game, you apply this a formula to both teams and take the average. During the season, college basketball teams averaged about 70 possessions per game. Gonzaga was one of the faster teams, as they averaged 74 possessions per game. It’s possible to get a more accurate possession count through play by play data.
To evaluate college basketball teams on offense and defense, we use points per possession as an efficiency metric. During the season, college basketball teams averaged 100.5 points per 100 possessions. To get from points per possession to college basketball rankings, you need to adjust for strength of schedule. There are many ways to adjust for strength of schedule. To adjust for strength of schedule, Pomeroy uses the least squares method. This is also the basic idea behind linear regression, the data science technique most often used to find the correlation between two variables. This least squares method also drives the team rankings on the Sports Reference sites. They call it the Simple Rating System (SRS), and this method assigns a rating to each team in college basketball. The difference in the rating between two teams gives a prediction for a future game. To perform this calculation, the computer changes the ratings for all 353 teams until the ratings meet a criteria. This criteria is that these ratings minimize the error between the prediction from the ratings and actual game results. Pomeroy takes it one step further as he considers offense and defense for each college basketball team. Instead of 353 variables, his code changes 706 variables to minimize the error to the efficiency by points per possession in games. Since these variables get solved for simultaneously, the offensive rating for Gonzaga depends on 705 other offensive and defensive ratings. In his calculations, Pomeroy puts more weight on recent games. After performing these least squares calculation, you get the offensive and defensive rankings on kenpom.com. These two numbers get combined into his team rankings.
With offensive and defensive ratings based on points per possession, we can now make predictions for games. Let’s use Gonzaga against Michigan State as an example. First, consider what the offensive and defensive ratings mean. For example, if Gonzaga has a rating of 115 points per 100 possessions, then they are expected to score 115 points per 100 possessions against an average college basketball defense. As another example, Michigan State might have a defensive rating of 90 points per 100 possessions. This means that they’re expected to allow 90 points per 100 possessions against an average college basketball offense. To make a prediction between Gonzaga’s offense and Michigan State’s defense, you have to consider that Michigan State’s defense is much better than average. To do this, consider the deviation of a team’s rating from average. To simplify the math, let’s use an average efficiency of 100 points per 100 possessions. Gonzaga’s offense is 15 points better than college basketball average, but Michigan State’s defense is 10 points better than average, both per 100 possessions. A common way to make a prediction is that Gonzaga’s offense will score 5 points per 100 possessions better than average. This is because 15 (Gonzaga’s deviation from average on offense) minus 10 (Michigan State’s deviation from average on defense) is 5. Gonzaga is predicted to score 105 points per 100 possessions against Michigan State. If you scale this efficiency to 70 possessions for a game, this implies Gonzaga will score 73.5 points. You can do same calculation for the other matchup. Suppose Michigan State’s offense has a rating of 111 while Gonzaga’s defense has rating of 93 (both measured by points per 100 possessions). You can work out that Michigan State’s offense is predicted to be 4 points better per 100 possessions. While I have assumed 70 possessions in this game, you could assume a different number, especially if Gonzaga plays faster than average.

Conclusion: Embrace the Madness

Filling out an NCAA tournament bracket is a thrilling experience that combines sports knowledge, data analysis, and a touch of luck. By understanding the principles behind bracket simulators, analyzing regional matchups, and identifying potential upsets, you can significantly improve your chances of success in your office pool. Remember to embrace the madness and enjoy the unpredictable nature of March Madness!

While aiming for a perfect bracket is admirable, it's essential to remember that upsets are inevitable. The key is to identify the most likely upsets and strategically incorporate them into your bracket while still prioritizing higher-seeded teams in the later rounds.

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