Setup

We will go through each proposed “system” starting with the current system, followed by the switching to the top seed hosting the 4-team regional, ending with the wildly different system in which the top eight teams host the bottom eight teams in week one followed by the top four remaining seeds hosting the bottom four remaining seeds.

We’ll look at average travel distance, amount of flights vs. busses—NCAA requires teams to drive if a tournament game is less than 400 miles—and total travel distance.

Current System

The current system has been in place since 2003 and sees the 16 team tournament divided in four neutral regional sites with four teams each.

Since 2014, 15 different arena have hosted regionals led by Scheels Arena in Fargo, North Dakota—2015, 2017, 2019, 2021, 2023, and 2025.

Under the current system, Denver is one of the “biggest loser” having to fly for 9 of their 11 of their regionals (81.8%). The only two instances of being able to bus were the two times they hosted a regional in Loveland, Colorado in 2022 and 2026. Michigan and Notre Dame are also among the biggest losers both being forced to fly to 85.7% of regionals (6 of 7).

The “biggest winner” in this system is Providence who was able to bus to 7 of their 8 tournament sites (87.5%), the only exception being 2026 when they were sent to Sioux Falls, South Dakota. Quinnipiac is also a big winner in this system also being able to bus to 7 of their regionals.

In total in the current system, there were 102 instances of teams busing to regionals (53.13%) and 90 instances of teams flying to regionals (46.88%) out of 192 opportunities.

Every team—except Air Force—with multiple NCAA tournament appearances in this span were able to bus to at least one of their appearances. Penn State and Boston College were the only teams with 3+ appearances to not have to fly to any regional.

Average combined travel distance for all teams in the current system: 520.01 miles

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Top Seeds Hosts

Next up we’ll look at travel if the top seeds host each of the four regionals. In total, 22 different arenas would have hosted regionals—up from 15 in the current system in that span—with Conte Forum (BC), Magness Arena (DU), Ralph Engelstad Arena (UND), and Mariucci (Minnesota) each hosting 4 times.

This new system decreases the number of teams flying >50% of their appearances from 19 to 17, a quite small decrease.

Let’s look at how this system changes the amount of travel and the amount of flights. Denver and Minnesota State are big winners in reducing their travel distance, with Denver traveling on average 418.257 miles less and Minnesota State traveling 376.881 miles less. In terms of flights, Michigan drops their number of flights by 4.

On the other end of the spectrum, Cornell’s number of flights increases by 5 and their average travel distances increasing by 361.116 miles.

Overall this system increases the total number of flights from 90 to 97 and drops the total instances of busing from 102 to 95.

Average combined travel distance for all teams in the a system in which the top seeds host: 498.51, a decrease of 21.49 miles.

Top 8 Seeds Host, Bottom 8 Seeds

In what would be a major shift in the system, we look at what a system that sees the Top 8 seeds, host the bottom 8 seeds at home in weekend one of the tournament. In weekend two, the top 4 remaining teams would host the bottom 4, a week later. For simplicity we are utilizing the first round matchups of the last 12 tournaments. In this “new” system the amount of top seeds winning would most likely increase but we will assume the first round winners will be same for simplicity.

31 different venues would host first round games in this format in the same span. If you just look at post the last 6 tournaments we’d only see 20 different venues with Minnesota hosting in 5 of 6 and Michigan, Denver, and North Dakota hosting in 4 of 6.

19 different venues would host 2nd round games, led by Denver who would host in 7 of 9 second round appearances, along with BC hosting all 5 of their 2nd round appearances and BU hosting 4, busing to UMass Lowell for 1, and flying for their other.

Looking just at the past 6 years, just 12 teams would host, led by BU, BC, Michigan, and Western Michigan who would host 3 times each.

Denver is the biggest beneficiaries of this system bring their total number of flights over 12 years from 9 to just 4 and dropping their average travel differential by 586.195 miles. Cornell goes from 1 flight to 5 in their 7 tournament appearances and increasing their average travel by 597.137 miles.

A downside of this system, is that it moves the second week of the tournament up against the Final Four—which is the reason why currently there is a week between the regionals and the Frozen Four. Also for fans, it is another weekend of travel and potentially hotels if they make it to the next round.

The total number of flights would increase from 90 to 102. Average combined travel distance for all teams in the a system in which the both rounds are hosted by the top teams host: 526.54, an increase of 6.54 miles.

Analysis

The only season the “Top Seeds” hosting the regionals would decrease total flights would be 2021. On only three occasions—2015, 2017, 2022—would the top remaining seeds hosting the 1st and 2nd rounds decrease the number of flights.

In the current system, 36 different teams have had to fly, just 18 have had to fly more than once. In the top seed hosted regionals, 41 different teams would be required to fly—23 more than once. When the top remaining seeds host over two weekends 38 teams require at least 1 flight and 22 would have to fly more than once.

Conclusion

Looking exclusively at flights and travel distance, for the vast majority of teams, on average, all three systems are not that different. The home-hosted regionals and the home-hosted 1st and 2nd round games would have issues for non-fans and potentially local fans alike being to get to tickets, due to limited capacity compared to current set up at some places. For example, Quinnipiac would be hosting 3-5 games at a 3.2k arena. If we’re going to change anything with host, let teams bid their own arena if it meets the NCAA minimum of 5,000 seats.

Beyond capacity, ticket prices would also be a concern. Single game tickets costing twice or even three times what normal tickets to a team’s game given the NCAA requires a minimum of $100,000 guarantee2 and the NCAA does not allow complimentary tickets to its championships3. As a result students would need to pay their own way into the game or schools or alumni groups would need to front the costs

None these solutions solve the biggest issue in getting people into arena, game schedule. Both of these systems would still be tied to a game schedule that prioritizes giving games their own TV window, leading to games at 2pm on Thursday. In a world where more teams would get home games, who are the ones that will be forced into the worse time slots? Surely not the teams that get a home tournament game once a decade. Getting a game schedule with more games at times that people could attend would do wonders to attendance. Additionally, the extra day in between game is a waste and makes it harder for people to travel. Going back to a Friday/Saturday, Saturday/Sunday schedule with games at 5 pm and 8pm local time would do extremely well.

Hosts

Arenas

27 different arenas have hosted regionals, while 8 arenas have hosted 5 times or more—SNHU Arena (10), DCU Center (10), MVP Arena (7), Total Mortgage Arena (7), Scheels Arena (6), Amica Mutual Pavilion (6), PPL Center (5), Van Alden Arena (5).

9 different eastern2 arenas have hosted, while 18 different western arenas have hosted regionals.

Schools

Each regional has a “host” school which takes care of the logistics of the regional and if the “host” school makes the tournament they will automatically be placed in that region.

In total 27 schools (and 2 conferences) have served as the host of a regional—in a few cases two schools have served as co-hosts, Yale and Fairfield and Yale and Sacred Heart in Bridgeport, and UMass and AIC in Springfield.

4 schools have hosted 7+ times: UNH (10), North Dakota (8), Holy Cross (7), Minnesota (7).

There are 27 instances where the host school made the tournament, led by UNH with 5 followed by North Dakota and Minnesota with 4 each.

In total 14 western teams have played in regionals in which they hosted, and 13 eastern teams have done so.

Of these 27 instances, 10 (7 west, 3 east) one seeds have hosted, 6 (3 west, 3 east) two seeds, 8 (4 west, 4 east) three seeds, and 3 (all eastern) four seeds.

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Visitors

One aspect of the neutral site vs. home regional debate centers on some teams having to travel more than others.

Schools

Since every team has a different number of NCAA tournament appearance, it’s hard to compare the raw number of times that a school has been forced to travel outside their “region.”3

There are 15 teams that have traveled—i.e., an eastern school playing in a western regional and vice versa—50% of their non-hosting appearances in the NCAA tournament. Of those 15 teams, six were western schools and nine were eastern schools.

Seeds

In terms of seeds, western teams are forced to travel more, however they may just be a product of more western teams in the tournament and by extension more potential first round intra-conference matchups to resolve.

For example, there were 12 instances where there were more western 1 seeds than western regionals.

Seeds and Schools

Of the 5 schools in this era with 5+ times as a 1 seed—Minnesota (10), BC (9), North Dakota (7), Denver (7), Michigan (5)—only Michigan (80%) and Denver (57.14%) have been forced to travel more than 12% of their times as a 1 seed. BC and Minnesota each being forced to travel once, and North Dakota never being forced to travel.

Conclusion

56 different teams have made the NCAA Tournament in the 16 team, 4 regional era—30 from the east, 26 from the west—of those 1 of the eastern teams has dropped to D2 (AIC) and 2 of the western teams have dropped the sport (Wayne State and Alabama-Huntsville). As a result, 54.7% of active teams with NCAA comes from the East.

In the end, the issue of the regionals, with regard to attendance, isn’t whether they should be at neutral sites or home-sites, it’s schedule. Games should not be scheduled at 2 pm on a Thursday afternoon.

If anyone wants the regional data I’ve included the table above which has the option to download the data.

Expected Goal Rates

Expected Goal (xG) rates are a good analog for shot quality, showing how well a team takes quality chances and how well a team limits their opponent’s quality chances. With regard to all situations, all but 3 teams are within the top right quadrant, meaning they are above average in xGA/G and xG/G. Of the three outside the box, BU is essentially the national average for both metric; Minnesota State is just below average in xG/G but are second in the nation xGA (2.47); and Penn State is a real outlier where they allow their opponents a lot of high quality shots per game but also record a lot of high quality shots.

In terms of Net xG: Denver (1.25), Cornell (1.13), and Minnesota (1.09), Maine (1.07), Quinnipiac (1.03), and Western Michigan (1.03) lead the pack, the only teams with a net xG over 1; Michigan State (0.96), Bentley (0.89), and Boston College (0.87) are next up, the only others with a net xG over .6; Minnesota State (0.58), Providence (0.42), UMass-Amherst (0.39), and Ohio State (0.36) round out the next group, the remaining teams above 0; and finally Boston University (0.00) and Penn State (-0.05).

xG vs. Goals

Unsurprisingly, all 16 teams are in the top right quadrant (or just barely outside it). Every team is scoring within a goal of what they’re expected to. BU leads the group, averaging .61 goals more than expected, while Providence is on the other end of things, averaging .64 less than expected.

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xG vs. Corsi

Looking at xG vs. Corsi For (i.e., Shot Attempts), we can see how good a team’s shots are. Michigan State is far and away this best team in this aspect, they are taking a lot of shots, and they are of high quality. All teams outside of Cornell and BU are above average in Corsi at even strength, and all teams outside of BU are above average in xG at even strength.

Penalties For/Against

Looking at PIM For in a vacuum doesn’t tell much about whether a team just happens to play in games that just have a bunch of penalties, rather than the team itself being undisciplined. BU games tend to see a fair share of penalties, however, many of those games occurred in the first half of the season and the Terriers’ penalty rate has lowered in the first half. It is interesting to see that the tournament teams are far more distributed in the quadrants in this plot than in the others we’ve looked.

Conclusion

Taking all these plots together, you can see that these 16 teams really are top teams. No team is a huge outlier relative to the rest. Even on the upper end, no one team is really dominating, there are groups and clusters of high performing teams. It should be a fun and exciting two weeks, of best-on-best hockey.

What’s Getting Called

There have been 3519 penalties called as of December 5th, 2024. Let’s break them down:

1553 (44.13%) were Restraining Fouls—Holding, Holding the Stick, Hooking, Interference, and Tripping.

879 (24.97%) were Physical Fouls—Boarding, Body checking, Charging, Charging the Goalie, Clipping, Contact to the Head, Elbowing, Excessive roughness, Fighting, Grabbing Facemask, Hitting After the Whistle, Hitting From Behind, Kicking, Kneeing, Roughing, Slew footing, and Throwing Equipment.

674 (19.15%) were Stick Fouls—Butt-ending, Cross-Checking, High-sticking, Slashing, and Spearing.

310 (8.81%) were Other Fouls—Bench Minor, Broken stick, Delay of Game, Diving, Embellishment, Faceoff Violation, Goaltender Interference, Handling the Puck, Illegal Equipment, Misconduct, Shot After the Whistle, Too Many Players, Unsportsmanlike Conduct.

103 (2.92%) are what we’re going to call Additional Fouls—Game Disqualification, Game Misconduct—fouls that are called in addition to another penalty.

Below is a count of all the penalties called as of 12/5/2024. Tripping and Roughing are at the top of the heap, with 576 and 417 called, respectively. Slashing (286) and Too Many Players (100) round out the tops of each category.

Interesting to note, Embellishment was called 29 times, but Diving was only called twice—once against Quinnipiac on October 18th and once against New Hampshire on November 2nd, both games refereed by Hockey East refs. Embellishment, as described in the NCAA Ice Hockey Rule Book, is called in conjunction with an opponent’s penalty, while Diving is a standalone foul.

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Conference Breakdown

Let’s take a look at roughly how many penalties are called by each conference’s refs. To do this we’ll assume that the conference of the home team are the refs for the game, we will also exclude independents from this analysis.

Hockey East (8.26 Penalties Called Per Game)

In Hockey East games (81 games), 669 penalties were called for 1703 minutes of penalties. This comes out to 8.26 penalties per game at a Hockey East rink. Hockey East has 20 more Slashing calls than any other conference, with 70. The conference is second in Embellishment calls with 9—second to Atlantic Hockey America, 12—but both Diving calls, so far this season, have occurred in Hockey East games.

NCHC (8.09 Pen/Game)

The NCHC refs (64 games) have called 1284 minutes of penalties (518 penalties), for an 8.09 penalties per game average. 85 roughing penalties have been called in NCHC games, the most among the conferences. Additionally, 66% of slew footing calls have been called in the conference (4/6).

CCHA (7.91 Pen/Game)

In 67 CCHA games, 530 penalties have been called for 1285 minutes, an average of 7.91 penalties called per game. The CCHA have the most Unsportsmanlike Conduct penalties called in their games with 23. They also have joint most Too Many Player calls at 21, tied with Hockey East, however the AHA and ECAC are not far behind with 20.

ECAC (7.88 Pen/Game)

69 ECAC games have resulted in 544 penalties for 1319 minutes—7.88 penalty calls per game. The ECAC has accounted for 67 hooking calls, 7 faceoff violations (no other conference has more than 3), and is one of only two conferences (AHA) to call a Hitting After the Whistle penalty (10). The conference is the site of the only Butt-ending, Clipping, Fighting, and Kicking penalties so far this season.

AHA (7.81 Pen/Game)

AHA games have seen 672 penalties in 86 games, (1655 penalty minutes), an average of 7.81 penalties called per game, the most total penalties called of any conference. The AHA has recorded 15 more Interference calls than any other conference (76), and 11 more Hitting From Behind calls (31) than any other conference.

Big Ten (7.21 Pen/Game)

The Big Ten has the least penalties called 375 in 52 games for 915 penalty minutes along with the lowest average penalties called per game with 7.21. The Big Ten is the only conference with multiple Game Disqualifications called (2), the ECAC is the only other conference with any.

Period Breakdown

The 2nd Period sees the most penalties (1361), followed by the 3rd (1139), and finally the 1st (1000). Only 19 overtime penalties have been called.

There seems to be no correlation to Too Many Player penalties, with each period roughly seeing about 36 (37, 39, 34).

Cross-checking sees an uptick in the 2nd (104) compared to the 1st (85) and the 3rd (87). Slashing also sees a big uptick in the with 47 more in the second (127) than either the 1st (80), 3rd (77) or overtime.

Team Breakdown

Augustana leads all NCAA teams with the most Tripping calls called on them, with 21—the greatest number of instances of the same penalty called on the same team. Below is a table shows the team that has committed the most of each penalty.

4 teams (Clarkson, St. Thomas, Stonehill, and Holy Cross) have had 20+ different penalties called against them, with Clarkson leading the NCAA with 21. Clarkson has received penalties this season for: Boarding, Charging the Goalie, Cross-Checking, Delay of Game, Elbowing, Embellishment, Fighting, Game Misconduct, Goaltender Interference, Grabbing Facemask, High-sticking, Hitting After the Whistle, Hitting From Behind, Holding, Holding the Stick, Hooking, Interference, Roughing, Slashing, Too Many Players, and Tripping.

RIT leads the way in Restraining Fouls (44) and Physical Fouls (30). St. Thomas has the most Stick Fouls (22), while Lake Superior State as the most “Other Fouls” with 13.

Player Breakdown

Felix Caron of RPI and Dominick Campione of Stonehill each have been called for 9 different penalties. Caron has been called for: Boarding, Tripping, Hooking, Cross-Checking, Interference, Contact to the Head, Game Misconduct, Unsportsmanlike Conduct, Misconduct. Campione has been called for: Boarding, Body checking, Cross-Checking, Hitting From Behind, Interference, Delay of Game, Roughing, Unsportsmanlike Conduct, and Tripping.

Expected Goal Rates

Expected Goal (xG) rates can show us how well teams are at taking quality shots and limiting their opponents quality shots. Noticeably, BU is below average in xGA/Game in “All Situations” while they are slightly above average at even strength, which helps to illustrate why their penalty kill has had such a low success rate.

The Top 10 teams in Net xG (xG - xGA) are: Maine (17.9), Minnesota (17.2), Denver (14.6), Quinnipiac (11.4), Michigan (11.0), Western Michigan (10.9), Ohio State (9.7), Arizona State (9.4), Bentley (9.3), New Hampshire (8.4)

The Bottom 10 in Net xG are: Northern Michigan (-22.2), Stonehill (-16.0), American International (-11.5), Ferris State (-11.3), Lindenwood (-10.8), Army West Point (-10.5), Merrimack (-9.2), RIT (-9.2), Augustana (-8.3), St. Cloud State (-7.7)

xG vs. Goals

By comparing expected goals (xG) to goals, you can illustrate how “Lucky” or “Unlucky” a team is. For example, Wisconsin is averaging 4.01 xG per game but only scoring 2.25 goals per game, the biggest gap in the NCAA. Eventually those rates should converge.

The biggest outliers are Wisconsin (1.76) Harvard (1.18) —the only two schools with an xG/Goal differential over 1—and Dartmouth (-1.41) — the only school scoring, on average, a goals or more over their expected rate.

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Corsi For vs. xG

Looking at Corsi For (i.e., Shot Attempts) vs xG, we can see how good a team’s shots are. Some teams, such as Minnesota State, take a lot of shots but are low quality shots, as a result these teams are less likely to score goals. Other teams, like Cornell and Colgate, don’t take as many shots, but when they do, they have a higher chance of scoring.

Penalties For/Against

Is a team undisciplined, or do their games just get “chippy”? Comparing a team’s PIM For and Against, you can see if a team is taking too many penalties or their games are just penalty fests. For example, BU leads the nation in PIM, however in two of their highest PIM games, vs Holy Cross (Oct 5th) and vs. North Dakota (Oct 25th), BU recorded 46 and 29 PIM, respectively, while their opponents recorded 37 and 20 PIM.

Conclusion

Taking all these plots together, you can get a fuller picture of where a team stands. For example: Denver—the undefeated, 12-0-0, reigning National Champions, are in the top right quadrants in each of the plots (the place you want to be), they’re taking more quality shots than they’re allowing, they’re taking a lot of shots, those high quality shots are going in, and they are disciplined. Taking all that together, it makes sense that they are where they are, at the top of the sport.

Freshman Goals Record (Men’s)

First up, Cole Eiserman and the freshman goal record. The record was set in the 1976-77 season by future Olympic Gold Medalist David Silk, who scored 35 goals in 34 games played. We don’t have game by game data pre-2003, so we will use Macklin Celebrini’s 2023-24 as an analogue, who is one of only 3 Terriers in program history to record 30+ goals in their freshman year—the third being Mark Fidler in 1977-78. Cole got off to a hot start, scoring 4 goals in his first 3 games, the first Terrier to do so since Shane Bowers in 2017-18. Through 5 games, Eiserman (4) is 2 goals off of Celebrini’s total (6). At his current pace, Cole is expected to reach 27 goals by the end of the regular season.

Freshman Defensemen Records (Men’s)

In 1989-90, Alexandre Legault set the program record for most points by a freshman defenseman with 30 pts in 43 games played. The record would stand for 33 years until Lane Hutson smashed the record, putting up 48 pts in 39 games played in 2022-23. Cole Hutson, Lane’s brother, is keeping it close, putting up 4 points in his first 5 games compared to Lane’s 6 in his first 5. Cole is currently on pace for 27 pts by the end of the regular season.

Lane was not the most prolific goalscorer at BU, though he does hold the freshman defenseman goal record at 15. It took Lane until his fifth game with the Terriers to record his first and average a goal every 2.2 games. Cole, scored in his first game as a Terrier, has the goal scoring ability to break Lane’s record.

Freshman Points Record (Women’s)

Lola Reid has started her freshman season like a rocket, with 11 pts in 9 games played—an astonishing 1.22 pts per game. BU Athletics Hall of Famer, Three-Time Olympic Gold Medalist Marie-Philip Poulin started her freshman year with a 2.00 pts per game pace in October, eventually recorded the school record of 47 pts in just 28 games. Reid is on pace for 42 pts by the end of the regular season.

Race to 100

Nobody has recorded 100 points in a Terrier jersey since Patrick Harper did so in 2019-20, Quinn Hutson and Ryan Greene have a chance to reach that milestone this season. Hutson is currently on 69 pts in 84 games with the Terriers and needs just 31 pts to reach the milestone, BU has 29 regular season games so he would need 1.07 pts per game to reach 100. Greene is at 72 pts through 83 games of his BU career, needing to record .97 pts per game to reach the milestone by the end of the regular season.

We will keep updating these trackers as the months go on, and add more if more records and milestone look to be in reach.

In this week’s edition of Stats’ Sheet we will explain a few of the most common hockey advanced stats: Corsi, Fenwick, PDO, xG, GSAx, and Game Score.

Corsi

Corsi1 is a statistic used to measure shot attempt differential at even strength. It is a good, simple, proxy for puck possession, given that most shot attempts are taken in the offensive zone. Positive Corsi indicates that a team spends more time in the offensive i.e., they are taking more shot attempts, while a negative Corsi indicates the inverse, that the opponent is taking more shot attempts. By contrast the more “traditional” plus/minus where for example a +1 rating doesn’t really tell you if a team was outplayed, and they just happened to score a goal. Corsi can be calculated per player or on the team as a whole. Corsi is made up Corsi For (CF) and Corsi Against (CA)

Corsi For (CF): Shot attempts for at even strength: Shots + Blocks + Misses

Corsi Against (CA): Shot attempts against at even strength: Shots + Blocks + Misses

Corsi (C): CF - CA

Fenwick

Fenwick is very similar to Corsi, in that it measures shot attempt differential at even strength. It differs from Corsi in that it does not include blocked shots. The reasoning for this omission, given by its namesake, blogger, Matt Fenwick, was that a blocked shot is either not a true scoring chance is on average of a worse quality than an unblocked shot. As a result, it is not a good predictor of scoring chances.

Fenwick = (Shots on goal FOR + missed shots FOR) - (Shots on goal AGAINST + missed shots AGAINST)

PDO

PDO, sometimes called SPSV% is an attempt to quantify “puck luck”. It is the sum of a team’s shooting percentage and save percentage multiplied by 10. Teams will usually end up with PDOs around 100. A team with a PDO greater than 100 is probably not as good as they seem, and a team with a PDO less than 100 is probably better than they appear.

PDO = (Shooting % + Save Percentage %) * 10

Let’s go through the above plot clockwise, starting from the top right:

• Top Right: "Lucky" Teams
  These teams have the best of both worlds, a high shooting percentage and high save percentage, meaning they score a lot per shot taken and do not allow many goals per shot allowed

• Bottom Right: "Fun" Teams
  These teams tend to score a lot but also allow a lot of goals, meaning their games often turn into shoot-outs

• Bottom Left: "Unlucky" Teams
  These teams have the worst of both worlds, a low shooting percentage and low save percentage, meaning they allow a lot of goals but also don’t score many per shot

• Top Left: "Dull" Teams

  These teams tend to be involved in games that are usually low scoring defensive/goalie battles, games that some would call “dull” or “boring”

Expected Goals (xG)

Expected Goals, or xG, is the hot, new, advanced stat in hockey. xG uses a statistical model based approach to determine the probability that a shot, given its location on the ice, the type of shot, the phase of the game, and other factors (each xG model may produce a slightly different xG for a given shot). It attempts to quantify how many goals a player or a team is expected to score. For example, a shot from the slot from a set power play has a higher xG (read: higher probability of a goal), than one from the blue line on a 3×5 through traffic. As can be seen below, shots closer to the net have a higher chance of being goals.

Goals Saved Above Expected (GSAx)

An extension of xG is Goals Saved Above Expected, or GSAx, a goalie stat that attempts to quantify how many goals a goalie has saved above (or below) they are expected to save. A positive value means that a goalie is saving more goals than the average goalie would. An example of a goalie’s GSAx (Mathieu Caron (BU), 2023-24) over a season is pictured in the table below. In this example, BU had a 22-2-1 record when Caron has a GSAx > -.7 and a majority of the team’s losses occurred when he had a GSAx ≤ -1.8.

GSAx = Expected Goals Allowed (xGA) - Goals Allowed (GA)

Game Score

Game Score is a metric created by Dom Luszczyszyn of The Athletic, the metric is originally a baseball stat created by legendary baseball statistician Bill James. It is a way to quantify a player’s single game performance. Luszczyszyn release his initial version of the metric in 2016 and an updated and improved version in 2020.

Game Score 1.0

The initial formula for the metric uses goals, primary assists, secondary assists, shots on goal, blocked shots, penalty differential, faceoffs, 5-on-5 corsi differential, 5-on-5 goal differential (i.e., +/-). The weights, as determined by Luszczyszyn, are meant to make Game Score roughly equal to points, to be easy for people to understand intuitively. The issue with this initial formula is that defense is underrepresented, resulting in defenseman being underrated. Additionally, defenseman performance is scored equally to that of forwards, resulting in a lower game score for them by virtue of the fact that they don’t score or attempt shots or take faceoffs as much as forwards.

Skater Game Score = (0.75 * Goals (G)) + (0.7 * Primary Assists (A1)) + (0.55 * Secondary Assists (A2)) + (0.075 * Shots on Goal (SOG)) + (0.05 * Shots Blocked (BLK)) + (0.15 * (Penalties Drawn (PD)) – (0.15 * Penalties Taken (PT)) + (0.01 * Faceoff Wins (FOW)) – (0.01 * Faceoff Losses (FOL)) + (0.05 * Corsi For (CF)) – (0.05 * Corsi Against (CA)) + (0.15 * Goals For (aka Plus) (GF)) – (0.15* Goals Against (aka Minus) (GA))

Goalie Game Score = (-0.75 * Goals Allowed (GA)) + (0.1 * Saves (SV))

Game Score 2.0

In 2020, Luszczyszyn redesigned his Game Score formula to better account for both shot quality and defense. With regard to shot quality, Corsi was replaced with xG which, as we’ve previously discussed, does a better job at quantifying shooting ability. To better account for defense, the formula was adapted to have different weightings for forwards and defensemen, boosting the weight for defenseman play-driving (i.e., xGF and xGA) to counterbalance the countable stats that forwards are more likely to produce. Also, a huge thanks to Stephen Zenack at for adapting Luszczyszyn’s model to college hockey. To show how this new version of Game Score is calculated, we will use BU’s game vs. Merrimack on February 9th, 2024, a 7-1 victory for the Terriers. Game Score can usually be broken down into four sub-metrics: Offensive Impact, Defensive Impact, Production, and Miscellaneous.

Weightings:

Forward Expected Goals For (xGF): 0.625
Forward Goals For (GF): 0.625
Forward Expected Goals Against (xGA): 1.75
Forward Goals Against (GA): 0.4375

Defenseman Expected Goals For (xGF): 1.7
Defenseman Goals For (GF): 0.425
Defenseman Expected Goals Against (xGA): 2.3
Defenseman Goals Against (GA): 0.575

Offensive Impact

Offensive Impact or Offensive Impact above average takes into account a player’s impact relative to the team and to the national average, to determine how far above the impact of the average player is their offensive performance. Note: All components of Offensive Impact are at EV

Offense = (Team xG when on ice - National Average Team xG when on ice)*xGF Weight + (Goals For (i.e., Plus) - National Average Goals For)*GF Weight

Defensive Impact

Defensive Impact or Defensive Impact above average takes into account a player’s impact relative to the team and to the national average, to determine how far above the impact of the average player is their defensive performance. Note: All components of Defensive Impact are at EV

Defense = (Opponent's xG when on ice - National Average Opponent's xG when on ice)*xGA Weight + (Goals Against (i.e., Minus) - National Average Goals Against)*GA Weight

Note: Defensive Impact is subtracted from the total in the final Game Score calculation (i.e. the “best” Defensive Impact would be - (National Average Opponent's xG when on ice)*xGA Weight) - (National Average Goals Against)*GA Weight)

Production

The “Production” component takes into account player’s points, with the primary assist having a greater weight than the secondary assist, and individual actions (i.e., individual xG). xG taking the place of Shots on Goal to more accurately quantify the player’s shooting ability.

Production = (0.75 * Goals) + (0.7 * Primary Assists (A1)) + (0.55 * Secondary Assists (A2)) + (0.5 * Expected Goals (xG))

Miscellaneous

The final component is “everything else” that a player does that is “countable”: blocked shots, faceoffs, and penalties

Misc = (0.05 * Shots Blocked) + (0.15 * Penalties Drawn) - (0.15 * Penalties Taken) + (0.01 * Faceoffs Won) - (0.01 * Faceoffs Lost)

When we put those all together, we get a player’s full impact on the game, as can be seen in the chart below (Overall Game Score can be found next to the player’s name).

Conclusion

There are countless other advanced stats and metrics that are useful in measuring various aspects of a player’s or team’s performance. In addition, plot and graphs can be generated combining xG and Corsi to illustrate which teams or players are taking quality chances compared to others, a plot with xG and Goals can show who are the “luckiest” players.