One area PHOG magic can be seen is in the number of close games KU wins at Allen Fieldhouse. Even more precisely, the number of end-of-game defensive stops KU gets.

On a Saturday afternoon in early February, 1997, the highly-favored Jayhawks were in a dog-flight with the Nebraska Cornhuskers. Tied 60-60, Nebraska had a chance to win it in regulation and upset the then-undefeated and #1 Jayhawks. KU used pressure defense and didn’t allow a clean attempt at the basket, forcing overtime which was won by Kansas. The video can be seen here.

Since that time, defensive stands by KU at home have always stood out to us. From that date – 2/1/1997 – to the present, we’ve calculated that KU has played in 49 games with the following criteria:

• KU is on defense
• Score is anywhere from KU +3 to a tied game
• Clock is under 30 seconds

Effectively, KU needs a defensive stand to either win or force overtime. Were it to allow the opponent to score, it would either lose or be at risk losing in overtime. Excluded from this are scenarios where KU is down, as it would have to either foul or force a turnover to have a chance at winning.

Of these 49 games, KU has gone on to win 44, for a 89.8% winning percentage. And while some of these are easier to win (when KU is ahead), they’ve also gotten 10 stops in tie games to force overtime while only giving up 4 game-winning shots. They’ve additionally given up a few shots to tie the score while eventually going on to win. Still, 44-5 with the games so close seems astronomically impossible if the aura of the PHOG weren’t real.

Another way to look at this is to take each possession where the above criteria apply. As these games sometimes feature multiple defensive-stand possessions, we arrive at 70 possessions where KU needs a stop to either win/force OT. We calculate that 59.5 of these possessions ended in success (KU fouled and allowed 1/2 FT’s on one possession, which we’ll call a draw), or an 85.0% success rate. Opponents score at 0.357 points per possession in these scenarios, indicating that KU clamps down on the defensive end, using the energy of the crowd to win these high-leverage possessions.

The five defeats are:

• 1/22/2004 – Richmond. The Spiders hit a midrange shot down 1 to win by 1 with under a second to go.
• 2/19/2005 – Iowa State. The Cyclones made a midrange shot in a tie game to win by 2. KU still had 5 seconds left to tie/win, but couldn’t convert.
• 2/3/2007 – Texas A&M. Acie Law buried a corner 3 and led the Aggies to an eventual 3-point win. Law’s 3-pointer put A&M up 1 and they got a stop when KU tried to retake the lead in the final 20 seconds.
• 12/31/2024 – West Virginia. Javon Small made 1/2 FT’s with 1.8 seconds left.
• 1/25/2025 – Houston. Incredible collapse by KU allowed this to happen. In fact KU got scored on twice to allow Houston to extend the game…first in regulation and then in the first OT (following a turnover by Mayo).

Some might also wonder about 2017 Iowa State. KU did lose but that game never saw such a scenario. Sure, it was close. But KU wasn’t tied/ahead needing a defensive stand in the last 30 seconds of the 2H/OT. They had the final shot in regulation (a miss) and trailed in the late stages of the extra period.

Some notable wins include the 2022 Iowa State game, when ISU took the lead (briefly) with 18 seconds left only for Dajuan Harris to hit a scooping runner and for ISU to miss the final shot as time expired. KU did allow a basket when it needed a defensive stand, but it also got a final defensive stand here.

KU gave up a 3 to UCLA in the 2011 season which tied the score, but would go on to win in regulation after Mario Little got fouled as time expired and hit a FT.

KU had a clutch stop against Texas in 2022 on Senior Day inside 30 seconds left. The Hawks would go on to win in overtime.

Connecticut had a game-winning 3-point attempt in the 2024 season that was missed at the buzzer. Many of KU’s defensive stands are just watching the opponent miss. Again, is this the PHOG?

The most epic scenario was in the 2016 Oklahoma game, the triple-overtime victory which saw KU get 5 crucial defensive stands throughout the end of regulation (missed front-end FT) and overtime periods.

Charting the Hawks began charting during the 2019 season, when the idea was formed to attempt to isolate player value by simply charting everything (good or bad) the player did while on the court. Since that time as more and more thought has gone into this exercise more information has been gleaned, but the gist remains the same as it did from the beginning. Axiomatically, if Kansas as a team wins by 10 points, the means the individual players cumulatively were 10 points better than the opponents. The team’s performance just is the sum of its parts, and from there we can see who KU’s most valuable players are.

Adjusting for quality of opponent plays a big role as well, which we needn’t get into the details of. But adjusting for opponent makes our comparisons between years more accurate. A player who out-performs an easier schedule should be graded in comparison to a player who played in a season with a monstrous schedule.

Every year since 2019, which comes to 6 seasons so far, we have a full season’s worth of games charted using video replay¹. Video replay allows for the charting process to be as accurate as possible. Seasons before 2019 are still charted where video exists, and the player-value estimates we arrive at shouldn’t be far off. Still, how far off are these estimates possibly off? How can we determine this?

We decided to take the season’s worth box score information from each of the previous six seasons, calculate player-value metrics using only this information, and then compare it to what we calculated from the charts. In other words, how much additional information is provided by watching games as opposed to simply taking box score data as the basis for player value?

We looked at two value metrics, PPGAB and Per100AB. We compared Offensive, Defensive, and Total breakdowns from what the box score showed vs. what the charted data provided. We calculated this for 58 rotation player-seasons (all players who got at least 10% of possible minutes in a season) so as to ignore small sample sizes/walk-ons. The results follow.

Offense

Starting with Offensive PPGAB, only one player of 58 had a per game score of more or less than 1.00 point. This was Jalen Wilson in 2023. Wilson’s higher score (+1.30 per game) was likely due to his ability to score without an assist and his ability to win extra offensive rebounds that may have been credited as team rebounds. In Per100 terms, this was +2.10 points per 100 better than the box score alone would dictate. Other players who out-performed their box score estimates were David McCormick in 2021 and Devon Dotson in both 2020 and 2019. For Dot, he scored a bulk of his points without an assist, something the box score-alone data would miss².

On the other side of things, 2023 K.J. Adams had a far worse offense than box score data alone would have indicated. Because Adams required assists for much of his scoring that season, he was charted as -0.63 PPG and -1.32 points Per100 compared to box score data. Dajuan Harris from that same season was also a poorer offensive weapon than the box score presumed, as was 2019 Quentin Grimes.

Defense

Not surprisingly, the biggest differences between box score data and utilizing the charting system showed up on defense. Without seeing video evidence and considering each defensive possession using charting, people wouldn’t know that Marcus Garrett was 3.12 PPG better on this end in 2021 and 2.44 PPG better in 2020. Garrett got unfairly maligned for the team’s relative struggles in 2021. In reality he was an elite defender who helped that team maximize its potential. In addition, 2024 K.J. Adams excelled in guarding the ball. A season after playing out of position, wing K.J. Adams used his quickness and size to shut down scoring opportunities to the tune of 2.70 PPG compared to his box score estimate.

The worst defensive seasons come from Dedric Lawson (2019) at -2.36 PPG and -4.09 Per100 and Hunter Dickinson (2024) at -2.34 PPG/-4.20 Per100. Both offense-first, slower moving big men gave up more points per possession than the team average, something that was confirmed by charting games. Other poor defensive seasons include Jalen Wilson in 2021 (-1.93 PPG, -3.93 Per100) and Lagerald Vick in 2019 (-1.85 PPG, -3.19 Per100). This matters, particularly when looking at player value from other sources (Torvik, CBB Reference).

Total

The following players were over 2.00 PPG better than their box score value metrics would indicate. Marcus Garrett (2021) at +2.98, Marcus Garrett (2020) at +2.76, K.J. Adams (2024) at +2.38 and Devon Dotson (2019) at +2.12. These aren’t small differences.

On the flip side, the four biggest declines in player value came from aforementioned names. Jalen Wilson (2021) was 1.93 PPG worse than his box score information would suppose. Dedric Lawson (2019) was -1.84, Hunter Dickinson -1.81, and Lagerald Vick -1.62.

Hunter Dickinson has one more season at Kansas. Can he improve his defense? Well, it depends. Jalen Wilson certainly did. He became a -0.33 per game defender in 2021 to a +1.96 per game defender in 2023. I doubt Hunter has this drastic of an improvement, but with the team’s added depth this upcoming season he needs to perform better in 2025 than he did in 2024.

Absolute Value

It makes no sense to average out the differences, since by definition each season some players’ value scores will improve and some will worsen after we take into account chart data³. But we can use absolute value to see what the average difference is, whether plus or minus.

For Per100 numbers, the average offensive score is off by +/- 0.62 points. This isn’t too bad. Using the normal distribution, we’d expect about 89% of players to have an offensive chart score within 1 point of their pure box score Per100s. For the most part, the offensive data you receive from other sources is reasonable enough to conclude how good a player actually is on offense.

But on defense, the average Per100 number is off by +/- 2.07 points. This is quite a large gap. Defense makes up half of the game, and the majority of defensive player value comes from what we call “defensive coverage,” or the ability to prevent the man you’re responsible from scoring. And while the eye test can tell you some things about who a roster’s best defenders are, it cannot quantify things like charting does.

Only about 37% of players have a defensive score within +/- 1.00-point Per100 of their box estimates. Meaning its more likely than not that a player is noticeably better or worse than his box estimate.

If we use the total Per100 value scores and contrast the box estimates and the charted scores, we see an average difference of +/- 2.05 points Per100. This is about the same as the defense-alone score. Again, because box score data doesn’t provide us any look into defensive coverage, value scores derived from box score data alone tend to have significant differences from what a fully-charted system provides. Charting matters.

Looking forward

What this information does is it provides us with the ability to use confidence intervals in assessing player value from older seasons. For instance, the 2006 season only has 11/34 full games available for review (alongside some highlight clips that can help fill some gaps). In total, we give the 2006 season an 88.2% video coverage score, with 100.0% being a full season worth of game video and 66.7% being only box score info. Given such a large gap, it wouldn’t be surprising to see players’ value scores changing by as much as +/- 0.50 to +/- 1.00 per game⁴ should more videos become available for charting. From 2024 back to 2006, here are the video coverage scores by season:

Yes, in recent seasons occasionally a play is missed here or there thanks to technical difficulties or the broadcast failing to get back from a reply in time. This should be immaterial to player value calculations.

It’s encouraging to see relatively high scores for most seasons in the 2010’s, indicating that there should be small margins of error throughout the roster in these years. But generally, the further back we go, the less accurate the value estimates are.

Closing Thoughts

Half of the game of basketball is contested on the defensive end. If a player cannot defend, he will give up value which gets missed without video/charting. While player-value metrics from other sources admirably attempt to give the best estimates they can, they’re inadequate in revealing exactly how valuable a player is. Charting the Hawks has shown that the average difference in what box score only data provides and what a fully-charted system provides is +/- 2.05 points per 100 possessions. Therefore, elite defenders are highly underrated and terrible defenders highly overrated when other systems assess the value they bring to teams.⁵

1. Technically part of the Wofford game in the 2019 season was missing, but for the most part 2019 is complete. ↩︎
2. However, play-by-play info would have this mostly covered. Box score data is simply the most basic form of statistical information from which player-value estimates can be calculated. Certain other systems, to their credit, attempt to utilize other sources of info. ↩︎
3. If we average out each season, we will arrive at a number close to 0. On the whole, it evens out. But we want to know each player’s component of value, not the team as a whole (which we already know). ↩︎
4. Recently partial game footage from that season was discovered, with a certain player’s value score improving by 0.39 PPG. It’s unlikely every game will be this dramatic or shift a particular player’s value score in one direction, but it does show the effect even one game can have on value scores. ↩︎
5. On/off data, as utilized by Hoop Explorer and Evan Miya, is intriguing but after looking at how they correlate to CtH’s value scores we see noticeable gaps. This doesn’t prove they’re wrong (perhaps we are), but it does give us pause about using them. These systems rely on on/off data that might not always be accurate and are very “noisy” given they are attempting to isolate player value just from what a team does while a player is on the floor. It doesn’t have the eyes that charting does. ↩︎

Power Ratings for 2024. To compare teams, subtract one team’s rating from the other. This will provide an estimated skill difference in points per game.

In basketball, depth is an oft-discussed aspect of a team but one that can be difficult to measure or quantify. The size of the rotation is part of what depth is, but when we discuss “depth” we also want to know how good the rotation is. A team which plays 9 to 10 guys who aren’t any good isn’t really “deep,” at least not in terms of our expectations for KU basketball. Thus, a metric named Effective Depth has been created for us to measure recent KU teams in terms of depth.

Effective Depth measures not only how many players on the roster play a certain percentage of minutes, it also filters out players who don’t reach a certain threshold of player value. See the table below.

To calculate Effective Depth, fill in the number of players on the roster that meet the criteria in each of the 9 spaces. The top left is the easiest to reach (and will therefore be the highest number in the 3 x 3 matrix) and accounts for all players which play at least 10% of team’s minutes and contribute a Per100 value score of at least -2.00 points. After filling each of the 9 spaces, divide the total number by 9 and this will be that team’s Effective Depth.

Effective Depth does a few things. Not only does it adjust for the skill of depth, it also does so on a sliding scale. A very deep team won’t just have 10 guys in the rotation, it will have multiple players which reach 40% minutes and also have a minimum value score which is at least positive. A player with at least +2.00 Per100 AB value and 40% minutes played will contribute a full point to the tally. Players who contribute fewer minutes and/or at a lower value score will contribute some fraction of a point.

Since 1993, or the most-recent 32 seasons of Jayhawk basketball, the average KU team has had an Effective Depth of 5.47. This number is scaled to rotation size, so anything north of 5 means KU has a roster which should be solid at all times. Anytime such a team would have to go to the bench to play lesser players, it shouldn’t have to expend too many minutes on them and thus can manage minutes to be consistently competitive.

The teams with the highest Effective Depth scores since 1993 are the following:

• 1993 (7.89 players)
• 2007 (7.11)
• 2008 (6.67)
• 2006 (6.67)
• 2010 (6.44)

The 1993 team’s value score estimates aren’t as complete as those in later years, so this season’s Effective Depth score might be inflated somewhat. If a few guys see their scores drop, this number could come down closer to the others. But it is still safe to say that the 1993 team had solid depth.

Other deep teams came during the Chalmers/Rush-era, when KU didn’t rely on any one star but was still solid enough to win three straight Big XII regular season and conference titles, earn two #1 seeds, make two Elite 8’s, and of course win the ’08 NCAA National Championship. Additionally, the 2010 team had great Effective Depth and was the most-recent Kansas team to have a score above 6.00 players. Since the 2010 season, KU just hasn’t been super deep¹. Blame recruiting, NIL/transfer rule changes, early entrants to the NBA, etc. but the simple fact is KU teams aren’t as deep as they used to be.

Still, it hasn’t been all bad news. The bottom 4 rosters in terms of Effective Depth were solid teams that made it work by having stars carry the load despite not having much depth.

• 2004 (4.56)
• 2017 (4.22)
• 2009 (4.11)
• 2005 (4.00)

The 2004 and 2005 teams carried over three solid upperclassmen from the Williams era (Simien, Langford, Miles) yet not much else. Self’s best recruits were only underclassmen or not yet on campus. And while the big three holdovers had success in these seasons, they could only do so much. The 2004 run to the Elite 8 was a good tournament showing; the following year we’d rather forget. The ’09 and ’17 teams each had two elite players, a few solid role players, and then after that tried to make do and win games. They were both second-weekend teams, however.

The above teams weren’t actually the bottom 4, but rather team’s 2-5 in the bottom 5. The team with the worst depth hasn’t been discussed yet. It was last season’s team, the 2024 team, which had Effective Depth of 3.67 players. Remember in order to add Effective Depth, a player must contribute time on the floor and player value. For the 2024 team, only McCullar, Dickinson, and Adams could do both. Harris and Furphy did add a little value (as they were better than a -2.00 player), but the 2024 bench gave KU no Effective Depth. This is why KU struggled to maintain leads and was so bad after McCullar got injured. It decimated an already thin rotation. If we compared the 2024 roster’s Effective Depth to all others in the most-recent 32 seasons, it has a z-score of -1.90 or percentile of 2.9%. Your eyes weren’t lying. The 2024 roster was an outlier in terms of how thin and weak it was.

That’s also why Self said in the offseason he wanted a roster with “8 starters.” One way to look at Effective Depth is that it measures how many (KU-level) starters are on the roster. An Effective Depth of 5.00 should be considered the median score. Such a team’s depth is okay, neither great nor terrible. Anything below 5.00 and the roster is thin with certain players logging minutes despite not being quite up to the level KU fans would want. A score above 5.00 means KU has more depth and is likely bringing talented-enough-to-start players off the bench.

Looking at the bright side, the 2025 team is expected to have around 6.00 Effective Depth given preliminary projections². Even if these projections don’t fully hit, KU will certainly have a deeper team than it did last year, and should also be about as deep or deeper than it has been at any point over the last 15 seasons.

Edit Note: Now that season has begun, the 2025 season will show updated scores.

Effective Depth by Season:

1. KU has had good depth at times since 2010, including its 2022 National Championship team. It just hasn’t had elite depth since 2010 or earlier. ↩︎
2. KU’s top 6 should be very talented and we’d expect 1-2 others contribute as bench rotation players. ↩︎

Every 2 years when the Olympics come around and events start awarding medals, a debate is sparked on how to represent which country is performing best. You can sort the list by gold medals or total medals, but neither is the best way, as the former way undercounts non-gold medals while the latter way undersells the prestige of the gold medal.

Enter “weighted medal count.” In doing some quick research, I came across a few proposals that have been made. First we will start with the 3/2/1 method, also called the Swedish method. Golds get credit for 3, silvers 2, and bronzes 1. This equation would have one silver and one bronze equating to one gold. A different method that strengthens golds is the 5/3/1 method, also called the English method. 5 for gold, 3 for silver, and 1 for bronze. This would require one silver and two bronze medals to equal one gold. Another method is the 4/2/1 proposal, which requires two silvers for one gold and two bronzes for one silver.

Each of these methods is fine and would be better than what is implemented now (no weighting system). But none are perfect as they have been created ad hoc. The thing is, the correct answer is already out there if you’re willing to use some reasoning and do some math.

Let’s start with the reasoning. What is the difficulty of winning a gold medal compared to a silver or bronze? Let’s assume there is a competition with 100 participants, with medals for first, second, and third. Let’s just assume the winning time is one minute, zero seconds (1:00). Thus, of everyone who raced, only one person got a time of 1:00 or better.

Now we get to the math. The multiple column is calculated by taking the total participants by the number of participants to finish in 1:00 or better. In our example, let’s assume second came in at 1:01 and third at 1:04. Now, let’s fill out the chart for the top three places.

If we filled out this chart we’d get each finisher’s time and multiples getting smaller and smaller until it reaches nearly 0. But we are only concerned with the top part of this table. The multiple helps us see what we intuitively know. When an athlete competes we judge his performance by how many of the others he is better than. Getting first is the equivalent of beating 100-fold of the field. Getting second is equivalent of beating 50-fold of the field, and getting third is beating 33.33-fold of the field.

Let’s examine the relationship between the multiples. First place is 100, second place is 50, and third is 33.33. First is two times that of second, and three times that of third. Necessarily, second is 1.5 times that of third. And if we upped the total participants who raced to 1,000 or 10,000 or any other number–doesn’t matter–we’d still get the same relationship between first, second, and third place. This follows a 6/3/2 relationship or a 3/1.5/1 method.

Therefore, since a gold medalist’s winning time was achieved by half as many as the silver medalist’s, we reason that the gold medalist was twice as special. And the relation between every other participant is decided in this way. When it comes to weighting medals, this means 3 for gold, 1.5 for silver, and 1 for bronze.

This can also be intuitively remembered by the formula that two silvers = one gold or three bronzes = one gold.

Analyzing the Other Methods Again

Each method has its pros and cons. The Swedish (3/2/1) is correct with regards to the weight between gold and bronze (3 to 1), but the silver is too high in relation to both. The English method (5/3/1) doesn’t get any relationship perfect, but it does pull down silver closer to where it should be with regards to gold (although it makes bronze far worse than it should be). The 4/2/1 method gets the gold to silver relationship correct but not the bronze which it undervalues. Still, either of these proposals is better than the gold only method (effectively 1/0/0) or the no-weight method (1/1/1).

Using the 3/1.5/1 method, and weighting it so a gold medal = 1 (effectively making it the 1/0.5/0.33 method), here are the top 10 countries in weighted medal count at the close of recent Olympic games.

2026 Winter Olympics (Milan Cortina)

Another nice thing about the weighted system is that we can now use a “Percent of top” multiple allowing us to compare any two countries. In 2026, the US did better than every country except Norway, clearing third place and host Italy by 3.33 weighted medals.

2024 Summer Olympics (Paris)

In 2024’s Paris games, the US and China tied in the gold medal count, but this obscures the fact that the US of A won far more silver and bronze medals. In total, this is a whopping difference of 14.50 weighted medals. This is a lot less close than sorting by gold medals would make it appear. Notice also that no other country third and down was within 50% of the United States’ weighted medal count. America easily won the 2024 Olympics.

2022 Winter Olympics (Beijing)

Norway dominated the 2022 Winter games, finishing 5.67 weighted medals in front of Germany. This was the most-recent games that Russia was allowed to compete, and the US finished fourth and at 65.1% of the winner.

For the Projections made heading into 2024, see here. Date of Projection: 2/13/2025.

The model for projecting career majors for elite professional golfers has been simplified. While historic skill level and specific performance at majors was previously calculated using a pain-staking process, the new method is looking instead at historic pre-major betting odds and using that as the proxy for how likely a player was to win a major. Then, instead of building a “player age skill curve (PASC),” the age distribution of major winners since WWII was used. From here, a player’s recent trends and his career overall is factored in using multiples until we finally arrive at a career projection. Not only is this process easier to arrive at, it is also more objective than the prior one. Additionally, it ties to the (linked above) pre-major betting odds page, creating a symmetry within our databank of calculation/forecast systems. We let the market decide player value, the actual results of majors dictate the age curve, and then choose reasonable multiples to help smooth the data and hopefully arrive at solid estimates for golfers’ careers. Age refers to what the golfer will turn during calendar year 2025. The Projected Majors category refers to future majors. To calculate career estimated majors, add the player’s actual major total going into 2025.

We’ve included 137 golfers who are currently have listed betting odds in one or more major championship for 2025. There are others who weren’t included, though they are either non-competitive legacy winners or amateurs who qualified and don’t have much chance either.

The process for creating projections was multi-layered, but as noted earlier much more simple than the earlier iteration. A player’s forecasted future majors is predicated on his age and historic betting odds in the majors…that’s it. In one sense this forecast is less a projection of future majors and more a projection of how many majors the betting markets will think a player will win.

The toughest thing to account for in this model is whether or not a player will sustain elite play. Right now Scottie Scheffler has been clearly the world’s best golfer, and his major projections over the past three seasons have reflected that. As a golfer who will turn 29 in 2025, he is also in the prime years of a professional golfer’s career. This combination is what produces such a high projection. The model weights recent seasons heavily, and it is tough to forecast how likely it is that Scheffler sees a decline ala Jordan Spieth…a golfer who peaked in his 20’s.

Another difficulty is forecasting major qualification for LIV golfers. While it is built into the data to a degree, young talented golfers like David Puig and Joaquin Niemann will have a much better chance of winning majors if they can get into them. Niemann did well in LIV’s off-season and got into 3/4 majors in 2024. If he can continue to get into a majority of majors, his current estimate will be a lot more accurate than if he starts missing a lot of majors due to his league not having a direct path to the grand slam events.

Ultimately, it is the Scottie-era if he can maintain elite play. He’s projected to win 5.32 additional majors, which if he could get 5 more would get him to 7 for a career. This tie Arnold Palmer and cement him as one of the game’s greats. Others projected to win multiple majors moving forward include Tom Kim, Ludvig Aberg, and Collin Morikawa. Being young helps.

For 2025 itself, here is what the market is projecting in terms of total majors won (top 25 players only):

Note that this top 25 only accounts for 2.85 expected majors, so we’d expect one of the four to be won by someone not on this list. However, each major last season was won by a golfer in the top 10 in betting odds going in (Scheffler #1 – Masters, Schauffele T3 – PGA, DeChambeau T5 – U.S. Open, Schauffele #3 – British). We will also keep an updated market projection for the next major at our Current Championship Odds page.

The 2024 Kansas Jayhawks’ baseball season has sadly come to a close, following a competitive run to the Big 12 conference tournament semifinals yet missing an at-large bid. Still, spirits are high around the program, which has taken strides in Head Coach Dan Fitzgerald’s second year. For the Hawks to be a competitive baseball program, in conjunction with its improvements on the gridiron under Lance Leipold and its sustained basketball excellence, will be an exciting thing to keep track of in the coming years.

Still, even without Kansas in the 64-team field, the NCAA baseball tournament is a great and very underrated event. The double-elimination format works for the sport, but it shares a lot in common with the more-famous basketball version. A large array of schools–large and small–from different conferences each having a dream to do something special. High leverage moments, do-or-die situations which test the mental and physical fortitude of the athletes. Unpredictable results, with upsets lurking around the corner with the champion having to win multiple tough games to take home the trophy. By the time the College World Series gets around, the tournament has already had great moments. The first games will begin this Friday (May 31) and by next Monday the final 16 should be set.

When it comes to the field, here are the overall ranks of the programs which made this year’s tournament, coming into the tournament.

• Texas – 1
• LSU – 5
• Oklahoma State – 6
• Florida State – 7
• Arizona – 8
• South Carolina – 11
• Florida – 12
• Oklahoma – 13
• Mississippi State – 14
• Clemson – 15
• North Carolina – 16
• Oregon State – 18
• Arkansas – 19
• Vanderbilt – 22
• Texas A&M – 23
• Fresno State – 25
• St. John’s – 26
• Virginia – 31
• Georgia Tech – 32
• Alabama – 33
• Wake Forest – 37
• North Carolina State – 38
• Georgia – 39
• Tennessee – 46
• Connecticut – 47
• Western Michigan – 50
• East Carolina – 53
• Coastal Carolina – 55
• Oral Roberts – 59
• Tulane – 65
• Nebraska – 69
• Louisiana-Lafayette – 72
• Duke – 73
• Illinois – 81
• Southern Mississippi – 84
• West Virginia – 86
• UC Irvine – 87
• UC Santa Barbara – 90
• Oregon – 92
• Stetson – 93
• Indiana State – 95
• Kentucky – 114
• Dallas Baptist – 115
• Louisiana Tech – 120
• Indiana – 124
• James Madison – 125
• Central Florida – 127
• Virginia Commonwealth – 131
• UNC Wilmington – 146
• San Diego – 165
• Penn – 166
• Kansas State – 171
• Grambling State – 213
• Army – 219
• Evansville – 223
• Long Island – 224
• Nicholls State – 227
• Southeast Missouri State – 239
• Bryant – 255
• Grand Canyon – 281
• Wofford – 284
• Niagara – 292
• High Point – 292
• Northern Kentucky – 292

Of this year’s field, Texas is the best historic program (at #1) to make the tournament, however neither 2, 3, nor 4 made it. Texas is not having a great season either as it is a 3-seed (which is equivalent to a 9-12 seed in basketball).

Florida State, the #7 program all time, has famously never won a College World Series despite making it to Omaha 23 times. Is this the year the Seminoles finally reverse the curse? #15 Clemson and #16 North Carolina are trying to do the same.

East Carolina, #53 all time, is the best program in the field to never make the CWS. The Pirates are the final 1-seed in the 2024 baseball tournament, so they will host a regional and have a decent shot to get to Omaha. Kentucky, the second overall 1-seed this year, is #114 and has never made a CWS either. The Wildcats have an ever better chance of achieving this milestone for its baseball program.

Oral Roberts, the #59 overall program and Cinderella darlings from a season ago, are trying to make it back to Omaha. They will stay in state and play in the Norman regional. One difference is that the Eagles have a far worse record this season than they did last year, when they were under-seeded given their regular-season success in 2023.

There are three programs making their debut appearances in the college baseball tournament… Niagara, High Point, and Northern Kentucky. High Point is making its debut in the postseason at the D-1 level of any of the big 3 collegiate sports. The Panthers have been close to making the Big Dance in basketball (they are a relatively recent D-1 program), but they finally broke the seal in baseball.