It has been almost two full months since the 2021-2022 college basketball season came to a close with the Kansas Jayhawks’ come-from-behind victory over the North Carolina Tar Heels in the national championship game. But data does not have an offseason.

So far this spring, we have examined the NCAA Tournament résumés of the top men’s basketball college head coaches over the past 40 years. We have found that just based on the raw numbers, Michigan State head coach Tom Izzo is among the best. When it comes to performance compared to expectation, Coach Izzo is the best of all time.

We also explored and quantified the difficulty of both the draw and actual path of several notable teams over the past 20 tournaments. Interestingly, the data suggests that the 2022 Jayhawks had the easiest path to a national title of any team since 2002. Ironically, the 2021 Baylor Bears had the most difficult path in the same timeframe.

Finally, it is time to wrap up this series with a deep-dive into the overall odds of the NCAA Tournament as well as the odds of picking a perfect bracket in an office pool. As we shall see, it is possible to quantify the Madness of March.

Overall Tournament Odds​

Throughout this series, and in my annual NCAA Tournament preview, I have outlined a variety of tools that can be deployed in order to gain a deeper understanding of the way the tournament actually works. Almost all of them hinge on the use of Kenpom efficiency data to project point spreads and victory probabilities for any arbitrary tournament matchup.

Using these tools, it is possible to calculate the odds for any team to win any of the NCAA Tournaments back to 2002 when Kenpom began tracking this data. When all of this data is taken together, a big picture emerges as to the chances for any team to cut down the nets. Figure 1 below summarizes this data.


Figure 1: Odds for every NCAA Tournament team to win the National Title for the 2002-2022 seasons using both a linear scale (left) and a log scale (right)

As we can see, the best pre-tournament odds of any team in the last 20 years are just slightly better than 35 percent, which were the odds that Gonzaga had prior to the 2021 tournament. Other notable teams whose odds were greater than 25 percent include the 2002 Duke team, the 2015 Kentucky team, the 2008 Kansas team and the 2019 Virginia team.

Note that the difference in odds shown in Figure 1 for teams with similar pre-tournament Kenpom efficiencies are due entirely to differences in the tournament draws for each team. This topic was covered in detail in the previous installment of this series.

Of the seven total teams whose odds were greater than 25 percent entering the tournament, only two of those teams (Kansas in 2008 and Virginia in 2019) actually won the national title, which is right on the expected value of 2.13, based on the calculated odds. In other words, the #math checks out.

The bottom line is that winning the NCAA Tournament is hard. Even teams that finish the season with a Kenpom efficiency margin of +30.0 or greater average just one-in-five odds of cutting down the nets. A historically average No. 1 seed has odds of only 14 percent.

Lower seeded teams have much worse odds. The right panel shows the same data, but listed on a log scale. Interestingly, the total span of championship odds for the best and worst teams of the last 20 years extends 14 orders of magnitude.

For those scoring at home, the team with the estimated worst odds in the past 20 years was the 2005 No. 16 seed Alabama A&M team, which lost to No. 16 Oakland in the play-in game. My math gave the Bulldogs a one-in-97 trillion chance to win the national title.

Perfect Bracket Odds​

Over the years, many people have dreamed of winning their NCAA Tournament “office” bracket by somehow picking the results of all 63 games correctly (not counting the play-in round). Naturally, this has led many people to attempt to calculate those odds. The internet has a lot of articles that attempt this calculation. Most of them are wrong.

The most trivial way to make this calculation is to assume that all 63 games are coin flips and each team has a 50 percent chance to win each game. If this were the case, the odds of picking the perfect bracket would be about one-in-9.2 quintillion, which is the number that is cited most frequently. But it is actually a form of upper bound on the real odds.

The reason is that not all games are toss-ups. No. 1-seeded Kansas did not have a 50 percent chance to beat No. 16-seeded Texas Southern this spring. Kansas’ odds were closer to 97 percent. In other words, the coin that we use to make the calculation in the previous example is loaded. It would only be a “fair” coin in the extreme case.

As it turns out, the true odds to pick a perfect bracket are based on a certain weighted average (technically the geometric mean) of the odds for the favored team to win each tournament game. This weighted average is a function of the specific strengths of each team in any given tournament, which means that the odds of picking all of the games correctly vary from year-to-year.

The actual weighted average is around 58 percent (and not 50 percent) based on data for the past 20 tournaments. The value tells us that the real odds to pick a perfect bracket are closer to one-in-540 trillion. That is still a really large number, but it is 17,000 times more likely than the value that most people reference.

It is also possible to calculate the lower bound for the odds to pick a perfect bracket. These odds occur in the scenario where the favored teams win all 63 games in the NCAA Tournament. Effectively, the tournament would proceed according to “chalk.”

In this scenario, the weighted average of the hypothetical coin is closer to 68 percent, on average. Based on this value, the lower bound for the odds to select the perfect bracket has averaged about one-in-49 billion over the past 20 tournaments. The real odds are about 11,000 times less likely than this lower bound.

Perfect Bracket Odds Over the Years​

Now that it is clear that the odds of a perfect bracket have clear bounds and differ year-to-year, it is time to visualize what these odds have looked like over the years. Figure 2 provides this summary.


Figure 2: Actual odds of a perfect bracket compared to the "chalk" bracket where the favorite teams win each contest and the average odds resulting from a series of Monte Carlo simulations of each tournament.

As we can see from the orange bars, the “coin” weighted average is between 65 and 70 percent for the most likely, “chalk” brackets. This translates to odds between approximately one-in-1 billion and one-in-1 trillion. The best possible odds of a perfect bracket would have been in 2015 using a strategy of picking all of the Kenpom favorites to win all 63 games. In that scenario, the odds of being correct were one-in 4.3 billion.

When each tournament was then simulated, the odds dropped significantly, as shown by the striped green bars. Over the past 20 tournaments the geometric average of the odds for a perfect simulated bracket ranged from a high of one-in-60 trillion in 2015 to a low of one-in-10 quadrillion in 2006. Note that the “chalk” data and the simulated data are highly correlated.

It is interesting to note that the odds of selecting a perfect bracket were better in years such as 2015, 2019 and 2021. The odds were worse in years such as 2003 and 2006. In the previous piece in this series, I pointed out that the former set of years were ones where the bracket was particularly strong and the later years were ones where the bracket was particularly weak.

As a general rule, a stronger bracket should result in fewer upsets and it will therefore be more predictable with better odds to pick the perfect bracket. While there is a correlation between the simulated odds and the actual odds of a perfect bracket, that correction is quite weak. As the dotted green bars show, the actual odds of correctly picking the results of all 63 games have varied between one-in-3.2 trillion (in 2019) and one-in-350 quadrillion in 2022.

A comparison of the simulation odds and the actual odds essentially provides a way to quantify the Madness of March. In the years when the actual odds are higher than the average of the simulations (such as 2007, 2008 and 2019) the tournament tended to have fewer upsets total and a larger number of higher seeds advance to the Final Four. For example, 2008 is the only year in history where all four No. 1 seeds advanced to the Final Four.

The opposite is true for the years where the actual odds are significantly worse than the simulated odds. In those years there was an above average amount of Madness due to a large number of upsets, the occurrence of major upsets (such as a No. 15 seed beating a No. 2 seed) or both. These years also tend to result in lower seeds advancing to the Final Four.

To highlight a few examples, in 2011 a No. 8 seed (Butler) and a No. 11 seed (VCU) made the Final Four. In 2018, No. 1 seed Virginia lost to No. 16 seed UMBC (University of Maryland, Baltimore County) and a No. 11 seed (Loyola Chicago) made the Final Four. In 2021, No. 2 seed Ohio State lost in the first round and No. 11 seed UCLA made the Final Four. In 2022, No. 15 seed Saint Peter’s made the Elite Eight and No. 8 North Carolina reached the final game.

When it comes to unlikely events in the NCAA Tournament, No. 1 seed Virginia’s loss in the first round to No. 16 seed UMBC is usually the event cited as being the most “Mad.” However, the statistics (based on the Vegas spread) suggest that this type of upset should occur in about one percent of all games. In other words, we should expect to see a No. 1 seed go down about once every 25 years.

However, a No. 15 seed advancing to the regional finals (as Saint Peter’s did this year) has odds of roughly 0.18 percent, or one-in-550. This suggests that this type of event should only happen once every 140 tournaments. The math suggests that no one alive will likely ever witness such an unlikely NCAA Tournament run again in their lifetime.

That said, the magical run of the St. Peter’s Peacocks is still way more likely than ever predicting a perfect NCAA Tournament bracket.

With that, it is time to finally put a bow on the college basketball season. Until next time, enjoy, and Go Green.

The staff has done an amazing job getting visits we have heretofore been not able to achieve. Can he get us to the top of the mountain?

I think these agents by and large hate the deals. Maybe because they are not their clients?

What are your thoughts on long-term deals, like the ones to keep Mel Tucker (Michigan State) and James Franklin (Penn State) in place?​

Agent 1: Who other than Penn State would give James Franklin a 10-year deal? So what if he leaves? You’ll get another one. You’re Penn State. You can get another good coach.

(With Tucker), if you want to give him that deal after he beats Ohio State, then I get that. You get it on a Saturday morning and then you get smoked? Not even competitive. I think most athletic directors just have no guts. If you’re Michigan’s guy and (Jim) Harbaugh’s flirting with this Vikings job, go take the job. I don’t need to redo your deal. I’m going to get somebody who wants to be here. I’m not saying that’s what they should have done, but if more guys took that approach, coaches would realize, “Oh s—, Michigan is a really good job.”

Agent 2: Mel’s got a great situation. I can go 8-4 every three years and not worry about getting fired? That’s the greatest f——- job in the world. It’s really becoming irresponsible. I get that people say agents are the reason for this, but the fact administrators would do this shows how foolish they are. They’re spending TV money they don’t even have.

Agent 3: For both Mel and James, they got a great deal. But did the market dictate that those contracts should have been given? I don’t know. Were there enough schools going after Mel Tucker and James Franklin that this was the value over replacement? A lot of people in East Lansing were happy, but a lot of people also said, “Well, did Mel do enough yet to deserve that type of contract?” Same thing in State College. …

The reality is, how many 10-year contracts were signed this offseason and how many coaches will actually make it through those 10 years without getting fired? It almost never works out. As an agent, I can tell you that the market is very quickly overpaying for what it can get in terms of value over replacement for a good coach.

Agent 4: It’s a fool’s errand because things change. Nothing stays the same. A decade is an eternity in the sports business. Yes, buyouts are different, but with a longer team, the guaranteed money is much larger. Why would you ever put that type of liability on your institution? That’s not a very economically sound way of doing business. The demand for a return on that investment is enormous. What are the billionaires going to do when Brian Kelly doesn’t win a national championship in the first three years or when Mel Tucker doesn’t sniff being in a national championship game? Will they be happy with eight wins?

Agent 5: It was shocking to see guys get such huge guaranteed deals, but my prediction now is that we’ll just start to see more and more of them.

Agent 1: Some schools have to realize who they are. I don’t know if they’re trying to curry favor with alumni. Some ADs just like to say they’re the one who hired this guy.

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I write the Michigan State pages for the Athlon's preview. I don't have a say in how their Top 25 comes about. I just provide an update on strengths and weaknesses, compared to last year.

This is how their Top 25 shakes out (released today):

My question, Is there a limit to how many high level recruits we can give enough attention to on the same weekend? We have a minimum of 5+ high level official visits per weekend in June More the week of the 10th as that includes some commits.

Can we give that many recruits the full red carpet treatment? I know we will also have several 24"s etc on campus also. I guess it is much better in June that trying to manage visits during the season but it will be a huge test for the entire football organization to schedule and manage the process. Should be interesting.

U of M is -9 for the game. It seems like a lot considering the last two years. It must be their OL mostly coming back. They are a +14 against OSU and a lot of people seem to like that spot.

All signs currently point to current UMass Associate Head Coach Jared DeMichiel joining Nightingale's initial staff at Michigan State. DeMichiel and UMass won the NCAA National Championship in 2021 and won the Hockey East Championship in 2022. DeMichiel brings East Coast recruiting ties and was regarded as a top goaltender during his own college career at Rochester Institute of Technology.


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"Home run A++," according to our resident hockey expert @JD_Jerbear.

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I can’t keep up. I feel emotionally overwhelmed.

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Guess we're becoming the Cornell of the West?

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A highly regarded recruit on the west coast has locked in an OV to MSU for mid-June.

I am respecting the recruits wishes and not releasing the name and holding off the update article for now, but some of you will catch on pretty quick.

Any comment regarding Oregon Rivals board saying MSU putting together 7 figure NIL deal for Hicks?

I've never thought paying college athletes was going anywhere unless the women got an equal split for all sports. US Soccer might have just laid the groundwork for the wild negotiations to come at the Division 1 level.

US Soccer Agreement

"The respective unions will receive 90% of the FIFA bonuses paid at the 2022 and 2023 World Cups and 80% of the bonuses at the 2026 and 2027 editions. All of the funds paid out from those bonus pools will be split evenly among the two national teams. FIFA has announced that the entire bonus pool for the 2022 World Cup in Qatar will be $400 million, while the bonuses for the women's tournament in Australia in 2023 will be $60 million. In the previous World Cup cycle, the last-place men's team won more prize money than the first-place women's team."

Some information from Nightingale's first two weeks:

If you could be the best player in the world at one sport, what would you pick?

Discussed the Mucha rumor on here yesterday, official today:

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Back in April, the NCAA Men’s Basketball Tournament came to its conclusion with the No. 1 seed Kansas Jayhawks defeating the No. 8 seed North Carolina Tar Heels to claim the 2022 national title. The Jayhawks had to beat six other teams over three weekends to earn the right to cut down the nets, as has every NCAA champion since the mid-1980s.

But, not all paths in the NCAA Tournament are equally difficult. The entire structure of the tournament is based on the idea that the better teams (i.e. higher seeds) are given the easiest draw. But sometimes upsets occur and the occasional No. 15 seed wins a game (or three), busts the bracket, and clears the path for a No. 8 seed to survive to the Final weekend. Furthermore, not all No. 1 seeds (or No. 8 seeds or No. 15 seeds) are equally strong.

Fortunately, I have devised a way to calculate the difficulty of any given NCAA Tournament draw and path. The key idea is to replace any given team in any past NCAA Tournament bracket with a historically average Final Four team using Kenpom efficiency data as a guide. Tom Izzo’s 2005 Michigan State team is a great benchmark with a pre-tournament adjusted efficiency margin of 25.62, which is very close to that average.

By artificially placing this benchmark team in literally every position in every NCAA Tournament bracket back to 2002, it is possible to calculate that benchmark team’s odds to advance through every round of the tournament. Furthermore, it is possible to calculate those odds based on all possible paths at the beginning of the tournament (i.e. the “draw”) and on the specific “path” that the team in question actually followed.

Comparison to Historical Averages​

Before jumping directly into the numbers for a given tournament, it is helpful to understand the context for some of the numbers to come. To this end, I made a set of calculations based on a reference tournament consisting of an imaginary set of perfectly average teams that each are assigned the average Kenpom efficiency for their seed. It is then possible to calculate the difficulty of each teams’ “draw” or “path.” Figure 1 below gives the results of these calculations.


Figure 1: Difficulty of NCAA Tournament paths and specific draws for a simulated tournament where all teams are historically average for their seed

Three different data sets are shown. The green bars in the left panel represent the average draw for each seed. In other words, these are the odds that the reference Final Four team (2005 Michigan State) would have to advance to the Final Four if placed in the bracket as anywhere between the No. 1 seed and the No. 16 seed.

The green bars are a useful reference and also provide a sense of how much of an advantage it is to be a higher seed. In the No. 1 seed position, the reference team has a 27 percent chance to win the region. Those odds drop to 22 percent for a No. 2 seed placement, 19 percent for a No. 3 seed placement and 16 percent for a No. 4 seed placement.

For No. 5 seeds and lower, the odds start to level off between 11 and 12 percent. As one might expect, it is slightly better to be located in the “bottom” of the bracket, away from the No. 1 seed. In fact, of the lower seeds, the sweet spot is the No. 11 seed. This makes sense, as the No. 11 seed avoids playing the No. 2 and No. 1 seed the longest, which means there is a slightly higher chance that an upstream upset will make the actual path easier.

The other two data sets in Figure 1 represent the two extremes of specific paths that the reference team could take through the bracket. The left panel shows the most difficult “chalk” path where the reference team would face the highest possible seed in each round. For example, it would assume that the No. 1 seed would play the No. 16 seed, the No. 8 seed, the No. 4 seed and finally the No. 2 seed on the path to the Final Four. On average, the real path tends to be between four and six percentage points easier than the most difficult possible path for each seed.

Finally, the right panel of Figure 1 shows the odds for the “anti-chalk” path for each seed. As the name implies, this is the easiest possible path that each seed could take, assuming the maximum number of upsets. For the No. 1 seed placement, this would mean facing the No. 16, the No. 9 seed the No. 13 seed and finally the No. 15 seed. While this path is extremely unlikely, it would give each seed a significant advantage. Basically, any seed better than a No. 10 seed would have Final Four odds that shoot up to 55 to 60 percent.

Strength of Draw​

With that background established, it is now time to look at the results of the draw and path difficulty calculations for all NCAA Tournament teams stretching back to 2002. The full data set with reference to Final Four draw difficulty is shown in Figure 2.


Figure 2: Difficult of all NCAA Tournament draws from 2002-2022. A higher number is an easier draw, as it implies a higher probability of advancing to a Final Four.

In general, the shape of the curve in Figure 2 is similar to the green bars above in Figure 1. However, there appears to be more deviation at the extreme ends of the figure. Table 1 below shows the data for some of these teams at these extremes.

Table 1: Extreme examples of easy (top) and hard (bottom) NCAA Tournament draws since 2002.


The top of Table 1 shows a list of some of the easiest NCAA Tournament draws to reach the Final Four since 2002. The top two spots are held down by the 2002 Maryland Terrapins, who won the national championship, and the 2006 UCLA Bruins, who lost in the title game to No. 3 seed Florida.

A closer look at the makeup of each region gives clues as to why these two draws were so relatively easy. Figure 3 below compares the pre-tournament Kenpom efficiencies of the teams in the 2002 East Region and the 2006 West Region to the historical average values for those seeds.


Figure 3: Comparison of the Kenpom efficiency margins for the teams in the 2002 East Region and the 2006 West Region relative to historical averages (shown by the blue circles)

In both cases, No. 1 seed Maryland and No. 2 seed UCLA were grouped with other highly-seeded teams that were historically well below average. In 2002, No. 3 seed Georgia and especially No. 2 UConn were both very weak. To make the situation even easier, No. 8 Wisconsin and No. 9 Saint John’s were also surprisingly weak. In 2006, No. 2 UCLA was placed in a region with essentially a pair of weaker-than-usual mid-majors (No. 1 Memphis and No. 3 Gonzaga) as well as with a weak pair of No. 7 and No. 10 seeds as potential second round opponents.

A similar pattern arises for all of the teams listed in Table 1. A typical “easy” draw usually occurs when two or more of the top seeds in the region are remarkably weak. It also helps when the first round and potential second round opponents are below average.

The opposite is true for the teams at the bottom of the table, representing teams with surprisingly tough draws. In these cases, the region usually has two or three teams on the top few seed lines that are significantly above average. Teams that start the tournament in the First Four (play-in games) also tend to drift to the bottom of this table due to the fact that they have to play an extra game.

Overall, Table 1 also gives some hints as to years when the overall tournament field appears to have been relatively weak or relatively strong. For example, the 2003 and 2006 tournaments seem to have been particularly weak, as several teams from those years appear at the top of the table. Conversely, the 2015, 2019 and 2021 tournaments all seem to have been relatively strong.

Finally, I should note that Michigan State does have one team that appears in Table 1. The snake-bitten 2016 team ironically had the 17th easiest draw in NCAA Tournament history. That region also contained a fairly average No. 1 seed in Virginia and a very below average No. 3 seed in Utah. Unfortunately, that region also contained a surprisingly good (at least on that day) No. 15 seed called Middle Tennessee State.

For completeness, Table 2 below gives the remaining strength of draw data for all of Michigan State’s tournament appearances since 2002.

Table 3: Summary of Michigan State's NCAA Tournament draw difficultly since 2002.


As expected, Michigan State’s draws were generally better in the years when the Spartans were a No. 1 or No. 2 seed, with some notable exceptions. The Spartans’ draws in 2016 and 2009 as a No. 2 seed were both a little easier than the draw in 2012 as a No. 1 seed. The draw in 2014 as a No. 4 seed was relatively easy, as were the pair of ill-fated draws as a No. 10 seed in both 2002 and 2011.

Michigan State’s draw in 2021 was obviously the most challenging since it involved the First Four round. The 2022 draw was also difficult, but so was the Spartans’ draw in 2015 as a No. 7 seed, which resulted in a Final Four.

Strength of Path​

The ease or difficulty of the NCAA Tournament draw is important to compare initial brackets — once March Madness actually begins, what is more important is the actual path that a team takes along the road to the Final Four and beyond. Table 3 shows the difficulty of the actual NCAA Tournament path for the past 19 NCAA champions.

Table 4: NCAA Path difficulty for the last 19 National Champions.


Once again, the numbers in the table represent the calculated odds for an average Final Four team (such as Izzo’s 2005 club) to advance through each round up through the national championship game. Most of the past champions played a path where the benchmark team would have had between a four percent and nine percent chance to win the tournament.

Interestingly, this year’s champion, the Kansas Jayhawks, had a path that was twice as easy as the team in second place, the 2006 Florida Gators. As noted previously, the 2006 tournament appears to have had a very weak field. So, what happened in 2022?

On their way to the title, the Jayhawks defeated a No. 16 seed (Texas Southern), a No. 9 seed (Creighton), a No. 4 seed (Providence), a No. 10 seed (Miami), a No. 2 seed (Villanova) and a No. 8 seed (North Carolina). Kansas was certainly aided by drawing a No. 10 seed and a No. 8 seed so late in the tournament. But that is not all.

Creighton, Villanova and especially Providence were all relatively weak teams for their seed. In fact, the only opponent that Kansas faced in the entire tournament who was above average for their seed was No. 16 Texas Southern. This combination of factors resulted in a historically easy run to the title for the Jayhawks this year.

Ironically, last year’s champion, the Baylor Bears, have the record for the most difficult tournament path since 2002. Of Baylor’s six tournament opponents, only No. 3 seed Arkansas was below average for its seed.

Finally, Table 4 below gives the path difficulty data for a selected number of other Final Four teams, including Michigan State’s five Final Fours since 2002.

Table 5: NCAA Tournament Path difficulty selected Final Four teams since 2002.


As for paths to the Final Four, this year Kansas passed the 2021 Houston team for the easiest path to the final weekend in the Kenpom era. Prior to 2021, the 2005 Illinois and 2004 Connecticut teams held down the top-two spots.

Regarding paths to the title game, North Carolina’s 2016 team held the top spot until this year, with the 2006 UCLA squad and Michigan’s 2018 team rounding out the current top-four.

As for the most difficult paths to both the Final Four and the final game, Texas Tech’s 2019 team holds that record. Notably, the Red Raiders beat out both the 2011 VCU team and the 2021 UCLA team, both of whom advanced to the final weekend from the First Four.

Of Michigan State’s five Final Four appearances, the 2010 appearance was the easiest path, thanks in large part to No. 9 Northern Iowa’s upset of No. 1 Kansas in the second round. The 2015 team took the most difficult path of Izzo’s recent teams. Finally, the 2009 Michigan State team can claim the ninth most difficult path to the championship game back to 2002.

So far in this series we have performed deep dives into wins and losses, performance metrics versus expectations and the difficulty of various tournament paths. In the final installment of this series, we will explore the madness itself by looking at the overall odds for teams to win the tournament, as well as the odds to pick a perfect office bracket. Stay tuned.

Nightingale's annual salary is $470,000, up from his predecessor, Danton Cole, who also signed a five-year rollover deal when he was hired in 2017 and earned slightly more than $385,000 a year before he was fired in mid-April after five seasons, a 58-101-12 record and no NCAA Tournament appearances.

Nightingale's $470,000 salary includes base pay of $385,000 plus an additional $85,000 for making appearances on behalf of the university.


By comparison, Michigan head hockey coach Mel Pearson makes $400,000, including a $350,000 base.

Under Nightingale, Michigan State hockey will have $365,000 with which to hire two full-time assistants and a director of operations. Nightingale also will get the typical head-coaching perks, including hockey, football and basketball tickets, an automobile, and a country club membership.

As spring blooms in the state of Michigan, The Only Colors is taking a look back at the 2022 Men’s basketball season and the NCAA Tournament. In the previous installment of this series, we counted up NCAA Tournament wins, tabulated win percentages, and analyzed some round-by-round data.

What we found is that Michigan State head coach Tom Izzo is solidly in the top-10 of coaches and in all categories in the modern era (since seeding began in 1979). Furthermore, when it comes to Sweet 16, Elite Eight, and Final Four appearances, he personally has achieved more as a head coach than every school in the Big Ten over their history since 1979.

However, not all games and paths in the NCAA Tournament are created equally. For example, recently retired Duke head coach Mike Krzyzewski won a total of 101 NCAA Tournament games in his career. Of those wins, 25 of them came versus No. 15-and-No. 16-seeded opponents. There are only 25 coaches total since 1979 who have won 25 games in the Big Dance. For comparison, only six of Tom Izzo’s 53 career Tournament wins came against such low seeds.

So, while Duke and Coach K earned the right to play that many No. 15 and No. 16 seeds over his 36 total tournament appearances, it clearly gave him the opportunity to pad his numbers when it comes to the simple accounting of March wins and losses. Fortunately, there are more advanced ways to level the playing field by looking at metrics that measure performance compared to expectations.

Performance Metrics Summary​

In total, there are five performance-versus-expectation metrics that I tabulate for the NCAA Tournament. Two of these metrics are commonly used by others, two of them I created myself and one is another fairly simple accounting stat. I have explained each of these metrics before in detail, so I will only briefly introduce them here:

PASE (performance against seed expectation):

PASE is the “original” advanced NCAA Tournament metric. It measures the number of wins for each coach or team relative to the historical total number of wins per tournament for teams with a given seed. For example, No. 1 seeds have historically won 3.34 games per tournament since 1985. In order for a No. 1 seed to overachieve with a positive PASE score, it would need to win four games and advance to the Final Four.

PARIS (performance against round-independent seed):

PARIS is a metric that I created (prior to hearing about PASE) that measures almost the same thing as PASE. The difference is that I consider the historical win percentage for each seed in each round separately and not for the tournament as a whole.

PAD (performance against exact seed differential):

PAD is a variation on PARIS that I created, which takes into account the seed of the opponent for each tournament game. For example, playing a No. 15 seed in the second round is quite a bit easier than facing a No. 2 seed. PAD accounts for this difference, while PASE and PARIS do not.

PAKE (performance against Kenpom expectation):

PAKE is the other commonly-used metric that is similar to my PAD metric. PAKE accounts for the true strength of each opponent in each tournament game, regardless of seed, based on Kenpom efficiencies. However, this metric only goes back in time as far as 2002.

Chalk (+/-)

This is a simple accounting stat that measures the total number of games won by a coach or team relative to the situation where the higher seeds win all tournament games up to the Final Four rounds. Chalk and PASE give similar information.

With these definitions in mind, Table 1 below summarizes these NCAA Tournament metrics for 32 notable head coaches, sorted by PASE.

Table 1: Summary of performance versus expectation metrics for 32 notable men's basketball coaches through the 2022 season.


As we can see, when it comes to performance relative to expectation (i.e. seed) Tom Izzo is the best NCAA Tournament coach in the modern era.

Coach Izzo’s current PASE score of 14.94 is two games better than Louisville legend Denny Crum. The story for PARIS is similar. Coach Izzo’s current PAD score of 8.07 is a half-game better than Villanova legend Rollie Massimino. Izzo is in first place all-time in all three metrics and also is at the top of the leaderboard in the Chalk metric.

The only metric where Coach Izzo is not currently in first place is in PAKE, where he currently sits in fourth place behind Jim Boeheim (Syracuse), Roy Williams (Kansas and North Carolina) and John Beilein (West Virginia and Michigan). That said, PAKE does not account for Izzo’s first four tournaments, which included a Sweet 16 berth and three consecutive Final Fours.

For some additional context, Izzo’s current PARIS and PAD scores are higher than any other coach at any point in their career. In fact, the only time a coach achieved a higher PARIS or PAD score was Izzo himself following the 2015 tournament when his PARIS score was 9.31 and his PAD score was 8.75.

As for PASE, Krzyzewski did surpass Izzo’s current score back in 2001 with a score of 16.02. However, Izzo does hold the record for the highest PASE of all time with a score of 16.46 following the 2015 tournament. Note from Table 1 that Coach K retired with a final PASE of 11.60, and his PAKE is actually negative (-1.90) since 2002.

Comparing the Metrics​

Many other websites will reference the PASE and PAKE metrics, and they both certainly have value. But the PARIS and PAD metrics have certain mathematical properties that allow us to extract some additional interesting information. Specifically, the PARIS metric compares performance per round to the historically average performance for every team of the same seed in that round. The PAD metric is very similar, but it references the specific seed of each opponent, meaning that it is more specific to the actual difficulty of each game.

In other words, PAD more accurately reflects the true difficulty of a team’s path in the tournament. For example, did a highly-seeded team suffer an upset earlier in the bracket that then made the path easier for the other team in question? When PARIS and PAD are compared, the value represents the amount of “luck” that a team or coach has had in the opponents that they have faced.

This effect is best shown below in Figure 1.


Figure 1: Comparison of NCAA Tournament luck (as measured by the difference between PARIS and PAD) and true NCAA tournament performance relative to expectation (PAD)

Figure 1 compares the “luck score” (PAD subtracted from PARIS) to the PAD metric, which is indicative of the “true” performance versus expectation in NCAA Tournament play. Figure 1 includes data from all 666 head coaches who have appeared on the sidelines of at least one Tournament game.

The vast majority of these data points are clustered near the origin. However, several notable coaches appear in the area outside of this middle region. Each coach’s position on the graph gives information about the relative impact of “luck” on their tournament performance relative to expectation.

The upper right-hand corner of the graph highlights coaches with both positive PAD and luck metrics. In other words, on average these coaches have been both lucky and good. Most notable in this section of the graph are Krzyzewski, Beilein, Boeheim and the all-time king of NCAA Tournament luck, former Florida coach Bill Donovan.

Coach Donovan’s example helps to illustrate the meaning of the luck metric. History tells us that a No. 15 seed has defeated a No. 2 seed in the first round a total of 10 times in NCAA Tournament history. Naturally, this upset will usually favor the remaining teams particularly in that half of the bracket, as the nominally “strong” No. 2 seed has been eliminated. While at Florida, Donovan benefited from this type of upset in both the 2012 tournament (as a No. 7 seed) and in the 2013 tournament (as a No. 3 seed).

While Donovan certainly enjoyed a lot of tournament success, his performance relative to expectation was certainly “padded” later in his career due to some fortunate upsets in his part of the bracket. Similarly, Coach K, Beilein and Boeheim have been similarly “lucky” compared to the average NCAA Tournament coach.

Michigan State’s Izzo has also been slightly lucky over his tenure, but by just over half of a win. Rick Pinito (Kentucky and Louisville) and Crum (Louisville) were similarly good and more neutral on the luck scale.

As for other coaches of note: Massimino was almost as good as Izzo in the PAD metric, but the Villanova legend rarely caught a break with upsets in his bracket, while Williams was also good, but not so lucky. Former Arizona great Lute Olson was average in the PAD metric and almost as unlucky as Massimino. Meanwhile, Kansas’ Bill Self is also average based on PAD, but has been noticeably lucky.

Then, there are the coaches that have underachieved over the years, based on the PAD metric. Bob Huggins (Cincinnati and West Virginia) and Tony Bennett (Washington State and Virginia) both have negative PAD scores, but they cannot blame the difficultly of their tournament draws. On balance, both coaches have been lucky.

In a part of the graph all by himself is Rick Barnes (Texas and Tennessee). Coach Barnes’ current PAD score of -6.67 is the worst of all NCAA Tournament coaches as of 2022. He cannot blame luck either, as his luck score is -0.02.

For the final comparison in today’s installment, Figure 2 compares the PAKE metric to the PAD metric, as calculated since 2002.


Figure 2: Comparison of the PAKE metric to the PAD metric since 2002.

Figure 2 shows that these two metrics are strongly correlated, which makes sense. Both metrics are attempting to measure the number of actual wins compared to the number of expected tournament wins.

PAKE measures expected tournament wins based on the victory probability derived from Kenpom efficiency data (which correlates very strongly to Las Vegas betting lines). The seeds of the teams do not factor in at all. This is likely the most accurate way to measure performance versus expectation, but the data set is limited.

PAD measures expected tournament wins based on the historical data correlating win probability to the combinations of seeds playing in each game. As I have shown previously, the results of this calculation also correlate strongly to historical Vegas lines.

Most of the data points in Figure 2 fall onto or near the trendline. What is interesting about Figure 2 are the coaches whose data deviates noticeably from the trendline. Coach Izzo, for example, has a higher PAD score than his PAKE score. Mark Few (Gonzaga) and Bo Ryan (Wisconsin) similarly appear above the trendline in Figure 2, while Boeheim, Williams and Self all fall below the line.

I interpret this deviation as related to the accuracy of the seeding by the Selection Committee. If a coach has a higher PAD than PAKE, that implies that the Kenpom data indicates that the coach has more expected wins than is implied based on the seed combinations. That coach’s team, on average, has been better than their seeds imply (and/or their opponents have on average been worse). In other words, on average, that coach has been historically under-seeded. Izzo, Few and Ryan fall into this category.

The opposite is also true, as Figure 2 suggests that Boeheim, Williams and Self on average have received a higher seed than they deserve.

One of the concepts that we touched on above is the idea that not all NCAA Tournament paths are equal. In the next installment of this series, we will dive into this topic in more detail.

PWO Tyler Vroman. Had offers from Air Force and Army. He’s a 6’4 WR who has some athletic ability and runs a 4.4. Right off the bat measurable like that jump off the page. Really only played football as a senior, initially focused more on soccer, and had some alright production in a run dominated offense (497 yards and 6 touchdowns).

A kid with his size and speed I think absolutely has a shot to make an impact with some years in the program honing his skills. That combo of size and speed is rare.

LOL i was just sent this by my Seattle buddy. Its 2 weeks old so some of you might have seen it.

Warning: dont have volume up at work

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Dylan St. Cyr coming to MSU. Of course, you know who his mama is?