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.

Kansas has finished its pre-NCAA’s schedule, and now sits at 22-10 overall following a 13-1 start to the season. The Jayhawks are on a slide, performing as a -3.05 per game team in March (4 games) and below bubble-level (-0.43) over their last 10 games, in which they have a 4-6 record. This series of poor play can be assigned solely to the offensive end, where KU is performing as a -5.19 per game team over the last 10 contests. The struggles to score have coincided with injuries to 2 key players, first Kevin McCullar (who has missed 5 1/2 games over this period) and lately to Hunter Dickinson, who was in street clothes for KU’s 20-point loss in the Big 12 Tournament. Relatedly, KU’s poor shooting has plagued the team over this stretch. In the last 10 games, KU is shooting 24.2% from 3-point range, despite being selective on its outside attempts. There’s no denying it. Kansas is slumping.

Compared to their seasons overall, there are a few players who have not performed well in recent weeks, contributing to this decline in team strength. Hunter Dickinson, who has been a +0.19 per game player in recent weeks, has a +3.90 per game season value. Johnny Furphy (-1.86 recent, -0.38 season) and Kevin McCullar (+0.63 recent, +4.01 season) are the other two who have seen a sharp decline in their games. McCullar has the excuse of being injured during this period; certainly not 100% in the games he’s been able to play.

Of the others, its not like they’ve picked up their play in the absence of Kevin and Hunter. Back-ups like Jamari McDowell, Parker Braun, Nicolas Timberlake, and certainly Elmarko Jackson have struggled all season (aside from 1-off games here-and-there). K.J. Adams, for his part, hasn’t seen a dip in his solid play in recent games (he’s been steady all season). Only Dajuan Harris has turned up his game in recent weeks (+3.05 recent, -0.20 season), but this has primarily been due to his play on the defensive end, something less noticeable and more variable. We shouldn’t overlook Juan’s good defense by any means, but his offense over the last 10 contests is only slightly above-bubble (+0.30 per game).

What is the solution to this dilemma? It’s tough to say. But one thing is that KU needs its full roster available. Even despite the relatively poor play of Kevin and Hunter recently, having them healthy enough to provide 32+ minutes is crucial for the team, as their “D” games are way better than whatever the back-ups normally provide. More minutes from Jackson, Timberlake, and McDowell do not make the team better. Role player, such as Dajuan Harris and Johnny Furphy, likewise add more value when surrounded by stars like McCullar and Dickinson. Harris needs skilled scorers to find in good spots in order to generate assist-value. Furphy needs space to make 3’s or cutting lanes, something both Kevin and Hunter can provide by their ability to draw extra defensive attention and double-teams.

There’s been much discussion about injuries and poor shooting, and note that both have been the source of KU’s woes and both are related. KU’s been worse from 3 in games without Kevin (21.3%) than in games with him (27.2%) over the last 10, not to mention how much better of a shooting team they were when Kevin was 100%. In all games McCullar has missed (6 total), KU is a -0.44 team (+5.68 with him). Further, when KU doesn’t have a 7’2 skilled post scorer they can feed as part of their half-court offense, this hurts their FG shooting from inside the arc as well. If KU gets its two best players healthy by the NCAA Tournament, this will give its role players a better chance of making shots.

In closing, KU’s best team consists of its “main four” (McCullar, Dickinson, Adams, Harris) alongside whoever is playing best of the others. It may not always look pretty, but this line-up is most effective this season. If Furphy is hitting shots, he’s a great asset to the other four. But others have provided a spark as well, and as long as their roles are limited, the damage they will do to the team is not likely to be insurmountable. Still, KU will need consistent positive-value performances from its main four to make a deep run in the Big Dance. Benching K.J. this season because of “spacing” is a very dumb idea, and is probably only broached by the types of people who never played organized basketball past the third grade.

The 2024 Kansas Jayhawks finished the regular season portion of the schedule at 22-9, with a 10-8 conference record. Both of these marks are disappointments for a team which came into the season as the #1 team in the AP poll. To add injury(ies) to insult, KU’s two-best players on the season (Kevin McCullar and Hunter Dickinson) are questionable moving forward as the Jayhawks attempt to navigate the treacherous postseason waters.

Worst Case Scenario: No McCullar, No Dickinson

If KU were without the services of McCullar and Dickinson for the season, the team would have played below a bubble-team. Its other 7 rotation players average -2.13 points per 100 value. Even if you could manage the minutes to play the best players more (Adams, Harris) and the worst players less (Jackson, McDowell); the team would still struggle to be bubble-level with this 7-man squad. One glimmer of hope is that this collection of 7 players has performed better of late. In the last 10 games, these 7 players have been collectively a +2.46 per game team. While there’s no guarantee they can play this way without Hunter/Kevin (i.e. some of this value has been due to getting assists on baskets by these players), the team is actually more balanced now than it’s been anytime this season.

Possible Case: Getting 1 of them Back

Quite possibly, KU could get either Hunter or Kevin back but not the other. If Kevin’s out, this would look much like it has earlier in the season when he couldn’t go. Hunter would get a heavy diet of touches and look to score against or pass out of double teams. In the 5 games where KU played without Kevin, the Hawks averaged a +3.15 game score (+5.67 with him).

Getting Kevin back but not Hunter would be better than getting neither back, but there’s less info on how this would play out (the only time KU hasn’t had Hunter in is when he’s getting a breather, but that’s quite a big difference than not having him for a whole game or games). Kansas with Kevin but without Hunter would play smaller and likely attack defenses differently.

Best Case: Both Come Back

If both come back by the First Round of the NCAA Tournament, this KU team has a decent chance of making the second weekend. If KU can limit the minutes of Jackson, Braun, and McDowell to about 10-15 total per game, the other 185 will be filled by guys who are all better-than-bubble or trending near bubble-level. In conference play, Nick Timberlake is actually an above-bubble player (+0.02 per game). He defends adequately and has rebounded well for a wing-guard. His shooting has been a disappointment, but he limits his turnovers and doesn’t force a ton of bad shots. Johnny Furphy has dropped off in the last few games, but he is still near bubble-level for the season (-0.16 per game). Dajuan Harris has made the biggest climb in recent weeks, as he’s finally starting to defend at an all-conference level again. In total, his +1.42 per game value-score in conference play (+2.71 per game over last 10) puts him where he is adding value most nights. These three, alongside Adams/McCullar/Dickinson, make for a solid-enough team that can hang with any team on a neutral court. It may need a few breaks along the way, but this team would be worth paying attention to.

Assessing the Regular Season

KU was projected to be a +8.51 per game team, and it finished at +5.26 per game. This was a miss of 3.25 ppg, which is quite large in the grand scheme of things. At different points of the season the Jayhawks have had problems with guard-play, incoming players, depth, road games, injuries, and outside shooting. At the moment, all of these factors are contributing to what is looking like a disappointing season.

Big 12 Tournament

The Conference Tournament begins for Kansas on Wednesday, and the Jayhawks will likely face Cincinnati (could face West Virginia). As it’s looking more and more likely that both Hunter Dickinson and Kevin McCullar will miss this week to recover for the NCAA Tournament, KU is down to 7 scholarship players (of whom only 1 has produced above-bubble value this season). We will deign this group the “Scrub Squad.”

Utilizing the value-scores produced by each player, but looking at this from a per game and per 100 possession basis (while estimating minutes played), and additionally looking at the value-scores both from a season-long perspective and a recent-game perspective; we predict the per game value of each player should be around this:

• Dajuan Harris +1.26
• Elmarko Jackson -2.83
• K.J. Adams +3.48
• Nicolas Timberlake -0.70
• Johnny Furphy -0.60
• Parker Braun -0.83
• Jamari McDowell -0.57

The TEAM score is -0.80. Given Cincinnati is playing a touch above bubble-level, but also that the game is in Kansas City, our objective prediction is that this is a 50/50 matchup. Cincy will be hungry to make a run if it wants to make the NCAA Tournament (it may need to win 3 or 4 games in the B12 Tourney). However, the Bearcats aren’t a great outside shooting team. KU has a chance to outscore them from the perimeter, something the Jayhawks haven’t done to an opponent since…the Kansas/Cincinnati game in January. If KU faces West Virginia, KU should be an 8-point favorite.

Earlier drafts are here and here.

For a detailed look at KU’s excellent job of getting late-game defensive stops when the Jayhawks need them, click here.

It’s not exactly a secret that KU has a great home-court advantage. Since 2010, the Kansas Jayhawks have gone 227-12 in Allen Fieldhouse (95.0%). In conference play during these 15 seasons, KU’s record is 124-9, a 93.2% winning rate. For reference, in all 2-seed/15-seed match-ups in the NCAA Tournament since 1985, the 2-seed has a winning percentage of 92.8%. If this feels absurd, it’s because it is.

These records are a feature of three different things. First, Kansas has been very good in this span. Here is a list of accomplishments for Kansas in that span:

• 2022 National Champions
• 3 Final Four apperances
• 14/14 seasons¹ receiving a 4-seed or better
• 14/14 seasons making the Round of 32 or further (includes ’24)
• 11 Big 12 regular season titles
• 7 Big 12 tournament titles

So, a huge reason for KU’s success has been its strength. It has had consistently great teams.

The second reason KU has been so good at home is due to generic home-court advantage. In college basketball, the median home-court advantage is 2.9 points (according to KenPom)². Teams play better at home in a quantifiable way, one that will lead them to have more wins than if they faced the same opponents in neutral or true road games. Even if KU played home games at just an average gym, by virtue of it being the home arena, KU would have some advantage.

But, once we take into account KU’s strength through the years and the generic home-court advantage, we still see that Kansas has won more games in Allen Fieldhouse than expected. For instance, since the beginning of 2010, Kentucky has had 25 losses at Rupp Arena and Duke has had 23 losses at Cameron Indoor. Given that these three programs have, for the most part, also been consistently excellent, that’s quite a gap.

The third reason KU wins so often at home is because of “THE PHOG,” an effect we classify in ALL CAPS in homage to the sign that hangs in the Fieldhouse. And we can show this effect is real.

Utilizing KenPom’s subscription services, we note that it provides pregame estimates for each game since 2010³ (which is why we focus on 2010-present and not a different sampling of seasons). These pregame estimates will serve as our main data-points in order to prove that THE PHOG is indeed a magical place.

Pomeroy’s pregame estimates are devised by his algorithm and take into account each team’s relative strengths, the location of the game, and the broader data of all college basketball games to estimate the pregame win probability for each game. What makes this important is that it is neutral to any non-quantifiable factors, such as the mysterious PHOG of Allen Fieldhouse. Even though Allen Fieldhouse is special, Pomeroy’s algorithm treats it as any other gym, and because it does so, we can test to see if the Fieldhouse accounts for more wins than the Pomeroy algorithm predicted.

As we said earlier, KU has played 239 games in Allen Fieldhouse in the 15 seasons from 2010 to 2024. In each of these games, Pomeroy has made a pregame prediction. For instance, against Kansas State last night (March 5, 2024), Pomeroy’s algorithm gave KU a 84.4% chance of winning at the tip. After recording each pregame prediction in a spreadsheet, all we need to do is add up the cumulative estimated wins to see how many wins THE PHOG is worth. After all, we can confidently say that Pomeroy’s algorithm has accounted for KU’s high team strength as well as a generic home-court advantage.

Since 2010, Pomeroy’s pregame predictions have estimated that KU should have won 207.59 games, which is 19.41 games fewer than what KU actually won (227). This comes to 1.29 wins per season. THE PHOG effect is real.

To better show this, we tested to see how likely it was for KU to overachieve its home win total by 19.41 games over this period. Using the KenPom pregame predictions for each of the 239 games, we simulated these home games 10,000 times to see if we could reach 227 wins just by random chance. The results are below:

The average number of wins was 207.56, very close to the 207.59 total that was the sum of all KenPom pregame predictions. This shows that the 10,000 simulations was high enough to produce meaningful results. From here, note that the maximum number of wins produced through randomness was 223. Using the dataset of 10,000 simulations, we next show that the z-score for 227 wins is 4.09! or in the 100.00th percentile. A better way of understanding this is that we expect to have 227 wins or better only 1 time out of 46,123. This is further confirmation that THE PHOG is real and all must pay heed.

Betting Odds, A Free Lunch?

Betting on Kansas to win at home has consistently proven to be a winner. Using data going back to 2008, we estimate that if a bettor placed a $10 money-line bet on each home game⁴ since (276 total games), he’d have $224.81 today. This is a 20.7% annualized return. Additionally, one wouldn’t have gone negative at any time. Only twice in 15 years would this bettor have had a negative profit for that season (2011 & 2018). Betting on Kansas to win in Allen Fieldhouse has been more profitable and safer than the stock market.⁵

This shouldn’t be possible according to conventional theory. At some point, KU’s home-court advantage should be priced into the market. However, this strategy has only gotten more profitable in recent years. Since 2019, the average season has netted $21.24 (using $10 money line bets), more than the $13.22 average season net since 2008.

The market is likely doing the same thing as KenPom’s algorithm. It prices in the fact KU is usually the better team, and it sees a strong home-court advantage. Still, the market is trying to account for the fact that road teams do occasionally pull the upset but is still falling for seemingly-attractive prices for the road team that nevertheless aren’t equal bets due to how strong THE PHOG is.

Comparing Other Arenas

UPDATED FOR 2025

The fact Allen Fieldhouse continues to produce wins is a testament to its status as the world’s largest home court advantage.

In order to make this study more robust, we’ve begun to look at how much aura there is at other home court arenas. As this takes time to gather the data, so far only the following schools (and arenas) have been examined. These are ranked by z-score. In addition, we list Kansas and Allen Fieldhouse.

The teams selected are traditionally successful and have home courts which have reputations for being tough venues for opposing teams to win at. As these teams play different schedules and have unique strength-levels, comparing z-scores is the best way to isolate out the effects of the home courts themselves. While venues like Hilton Coliseum and Mackey Arena are quite strong (matching their reputations), none compares to Allen Fieldhouse.

The far-right column normalizes home games played in terms of 100 games. In other words, Allen Fieldhouse is responsible for 7.50 wins per 100 games KU plays there. Contrast this with Cameron Indoor, which has only earned Duke 0.28 wins per 100 games over the last 16 seasons (2010 – 2025).

Update Following 2025 Season

The Kansas Jayhawks did not have the best 2025 regular season, and ended up losing 3 home games for the first time since 2018. This tied the team’s highest number of home losses in the Bill Self era.

Overall, KU went 14-3 in Allen Fieldhouse in the 2025 season, or -0.219 games worse than KenPom expected (looking at his tip-off win percentages). So even though KU underperformed at home this season, it wasn’t by a whole lot.

Including the 2025 data and rerunning the simulations, we see the PHOG effect slip a bit but it is still very pronounced.

This shows that if you ran 10,000 random scenarios of KU basketball at Allen Fieldhouse since the 2010 season, you’d expect the team to win 237 games at most, and that only 1 time out of 17,205 would you expect KU to win the 241 total games it actually has at the Phog over this period. KU’s total Wins Above Expectation (WAE) is now 19.19, or 1.19 wins per season. So on average the PHOG is worth over 1 win per season when compared to an otherwise good college home court advantage.

Betting in 2025

Going 14-3 at home would have lost the hypothetical bettor taking KU with the money line each game. Had he bet $10 at the start of each game on the money line, he’d have lost $11.73 in the 2025 season. Now he’d still be up $213.08 since 2010 using this strategy.

Had he bet 10% of the pot each time (a strategy that would have turned $100 into $829.63 from 2010 – 2024), he’d have lost 12.3% of his incoming stake during the 2025 season (but would still be at $723.58 at season’s end if he started with $100 in the 2010 season). This would be a 13.17% annualized rate of return.

1. The 2020 season was cancelled a few days before Selection Sunday, and no official bracket was released. However, KU was a consensus #1 seed regardless at that point. ↩︎
2. KU’s generic home-court advantage on KenPom is 3.8 points, or 31st in D-1. ↩︎
3. KenPom users may wonder where the 2010 season’s pregame predictions are as Fan Match only goes back to 2011. However, KenPom’s win probability charts for each game go back to 2010, and we can see what the win probabilities were at tip off. ↩︎
4. Some of the buy-games didn’t offer a money-line option, and more recent ones are something like -100000 (meaning you’d have to lay down $10 to win $0.01) ↩︎
5. One difference is that you have to keep betting $10 even if you are negative, which makes this strategy inherently riskier than typical diversified stock market investments. An alternate suggestion would be to bet 10% of the portfolio (i.e. start with $100, bet $10, if you win [say $3 back], you have $103 in the portfolio and bet $10.30 for the next game). This strategy, since 2008, gets you to a $829.63 portfolio. This is an annual return of 13.6%. ↩︎
6. Since 2010, UNC has also played 2 home games at Carmichael Arena ↩︎
7. Since 2010, Gonzaga has played at least 1 home game at Spokane Arena ↩︎