(Inside Lacrosse Photo: John Mecionis)

Introduction

In the never-ending quest to determine who's the best, team sports like lacrosse only ever allow two teams to play each other at a time, which proves to be quite a limitation. Postseason tournaments allow us to crown a champion in the final game of the season, but do little to satisfy our curiosity during the season or to order teams beyond the champion.

In an attempt to fill that void, various rankings have been contrived ranging from polls aggregating responses solicited from a pool of people such as coaches or members of the media to computer rankings that rely purely on hard data such as the Ratings Percentage Index (RPI) used by the NCAA to select at-large teams for postseason tournaments or the LaxPower.com ratings.

It doesn’t take a nuclear physicist to figure out that while wins and winning percentage are important, looking at them alone isn’t sufficient. Who a team beat and the manner in which they beat them matter.

Determining the best individual performers at any position within a team sport is even more difficult. Especially in lacrosse, where very few players at the same position match up head-to-head and, even when they play against the same opponents, they are doing so with different teammates in different offensive or defensive schemes.

One place on the lacrosse field where players do go head-to-head at the same task is the face-off X. Thus, it presents a unique opportunity to apply the lessons learned from ranking teams and individuals in other sports and competitions to individuals in the context of a team sport.

However, when it comes to ranking and comparing face-off specialists, we are still stuck relying on some combination of winning percentage and subjective human judgement. Or at least we were.

On the Shoulders of Giants

The challenge of creating team rankings is that there are usually very few data points if one only considers wins and losses. Models are made more accurate by considering margin of victory or something as complex as cumulative win probabilities.

Face-offs are different; there are far more wins and losses over the course of a season. Top draw men usually take over 300 (and potentially as many as 500), but other than the winner and loser, there is relatively little additional information that can be easily gathered about each one.

Therefore, a rating system originally developed for rating chess players called the Elo rating system was an ideal starting point. Like face-offs, chess matches produce a winner and a loser by having two players attempt to accomplish an objective rather than having a score that is higher than that of your opponent. More specifically, my ratings are based upon the Glicko-2 rating system that was developed as an improvement on the Elo system. The Bayesian nature of the Glicko system proved to be especially helpful in dealing with all of the players who took a few dozen or fewer face-offs throughout the course of the season and the attempt to accurately calibrate the ratings with only a single season worth of data.

While statistics like groundballs, shots, goals and assists could all theoretically be used in a ratings system, that would create a couple of problems. In some cases, it's difficult or impossible to distinguish between those accumulated as the result of face-offs and those that occurred in other situations making the collection of data a nightmare. More importantly, giving a player credit for goals or assists starts to make the purpose of the rating murkier. The rating is designed to be a measure of player’s ability to win face-offs and only that ability. It is not an attempt to quantify all of the contributions face-off specialists make to a team into some sort of comprehensive value rating.

That is important because it gives the ratings a predictive capability that can be used to assess the ratings. Better ratings will do a better job at predicting the results of future face-offs.

Up Next: Creating the Ratings

Creating the Ratings

The first and by far the longest step in creating the ratings was compiling all of the data. It took countless hours of poring over box scores and play by plays to put together a comprehensive listing of the winners of all 12,118 face-offs that resulted from the 1605 different matchups of 313 different players that took place over the course of the 2014 season.

Glicko ratings are a Bayesian rating system, which means they require each player be associated with both a rating and a standard deviation, a measure of uncertainty about the rating. As is customary with Elo models, the initial rating for all players is set to 1500.

While the classic Elo rating system conserves ratings points by requiring one players rise in ratings to be equal to another player's fall and therefore keeps the median rating at precisely 1500, the Glicko rating systems are devoid of such a restriction. Thus, while 1500 will not the average rating, the distribution of ratings will be centered around 1500, so it can be thought of as a baseline comparison rating against which players can be said to be good or bad.

The suggested initial standard deviation for Glicko ratings is 350. For Division I face-offs, I have found that spread to be significantly larger than necessary, considering that no rating ends up being more than 1750 nor lower than 1150. A standard deviation of 350 would indicate that 95% of the ratings would fall within 700 points of the median in either direction. Thus, I chose to use an initial standard deviation of 250, which is still a very conservative value given the distribution of the ratings.

Elo and Glicko ratings are iterative models that require the choice of a ratings period which designates the frequency at which new ratings will be calculated. The creator of the Glicko rating system suggests that the ratings work best when each player participates in 10 to 15 contests during each ratings period. Since top face-off specialists commonly take at least that many face-offs in a single game, the ratings period was chosen to be a week.

In an attempt to convert the ratings back into a number that is more easily understood, I computed the estimated winning percentage of each player against an opponent with a 1500 rating to get an idea of how each one would do if they were to face-off against a more typical opponent every game. Unlike the standard winning percentages, these estimated winning percentages are directly comparable to each other.

Home Field Advantage

The phenomenon of athletes and teams playing slightly better at home is one that has been widely observed and documented in sports. Thus it is to be expected that face-offs are no different.

Using the compiled data and the ratings to control for the ability of face-off competitor, I estimate that home field advantage was worth about 13 rating points during the 2014 season. In the case of two equally rated players, that translates into the one playing at home winning 51.87% of the draws or an extra win roughly every 54 face-offs.

That 13 rating points of advantage due to home field is factored into the final ratings.

Up Next: McEwen FOGO Ratings

McEwen FOGO Ratings

Name School FO Win % Rating Std Dev Proj Win % 1 Charlie Raffa Maryland 68.55% 1722 32.49 78.25% 2 Kevin Massa Bryant 68.85% 1646 29.93 69.82% 3 Tyler Knarr Georgetown 65.81% 1642 36.24 69.37% 4 Tyler Barbarich Delaware 63.75% 1630 38.74 67.83% 5 Nick Saputo Drexel 62.04% 1617 30.49 66.27% 6 Stephen Kelly UNC 61.16% 1615 48.92 65.95% 7 Kris Clarke Hofstra 55.59% 1610 33.53 65.35% 8 Alex Kinnealey Colgate 60.73% 1608 34.71 65.05% 9 Drake Kreinz Penn State 61.14% 1606 38.89 64.85% 10 Brendan Fowler Duke 59.02% 1604 25.95 64.47% 11 Chris Hampton Denver 52.43% 1602 29.72 64.23% 12 Brady Dove Navy 56.40% 1592 35.71 62.96% 13 Casey Dowd Siena 58.82% 1591 31.20 62.80% 14 Dylan Levings Yale 58.10% 1589 33.73 62.59% 15 Kyle Rowe Stony Brook 62.47% 1586 33.05 62.11% 16 Graham Savio Loyola 55.92% 1585 32.84 61.97% 17 Phil Poe UMBC 59.95% 1582 32.31 61.61% 18 Sam Talkow Boston 56.74% 1581 36.03 61.39% 19 Liam O'Connor Notre Dame 58.03% 1579 28.31 61.16% 20 Gabriel Mendola Harvard 52.36% 1577 36.27 60.96% 21 Drew Kennedy Johns Hopkins 58.95% 1574 32.50 60.44% 22 Chris Daddio Syracuse 50.41% 1572 32.35 60.20% 23 Ryan Buttenbaum Lehigh 55.21% 1566 34.77 59.39% 24 Alex Daly Army 56.32% 1564 34.84 59.10% 25 Joseph Nardella Rutgers 63.19% 1564 35.80 59.09% 26 Danny Feeney Penn 49.82% 1563 32.80 59.03% 27 Doug Tesoriero Cornell 52.23% 1560 32.54 58.58% 28 Adam Yee Hartford 55.69% 1560 35.06 58.57% 29 Thomas Croonquist Villanova 50.45% 1557 32.19 58.13% 30 Justin Murphy Princeton 55.21% 1554 39.21 57.74% 31 Tim Edwards Canisius 61.14% 1554 39.24 57.74% 32 Austin Williams Harvard 47.75% 1554 41.64 57.67% 33 Tommy Capone Brown 53.55% 1551 37.42 57.28% 34 Joe Calvello UMass 56.76% 1547 38.35 56.73% 35 Mike Celmer Mt. St. Mary's 61.70% 1544 45.78 56.35% 36 Mick Parks Virginia 51.14% 1541 34.30 55.88% 37 Jamie Piluso High Point 59.39% 1541 35.01 55.82% 38 Dan Mazurek Binghamton 51.30% 1539 36.29 55.62% 39 Erik Smith Air Force 57.86% 1537 39.25 55.37% 40 Nick Ossello Notre Dame 44.32% 1530 45.04 54.32% 41 Hil Blaze Furman 54.84% 1529 38.61 54.20% 42 Ben Williams Holy Cross 53.14% 1529 34.54 54.13% 43 Zach Vehar Quinnipiac 53.54% 1518 34.79 52.60% 44 Michael Roe Fairfield 49.19% 1518 35.26 52.53% 45 Connor Russell Albany 46.13% 1516 32.97 52.23% 46 Dominic Montemurro Marist 54.93% 1515 34.01 52.22% 47 Tyler Mardian Delaware 49.49% 1515 45.72 52.20% 48 Mike Lanham Saint Joseph's 55.21% 1515 49.19 52.15% 49 Paul Riportella Marquette 45.91% 1514 41.23 51.95% 50 Jake Shapiro Hobart 45.14% 1513 34.39 51.84% 51 Jake Withers Ohio State 50.92% 1512 37.24 51.76% 52 Justin Evans Mercer 56.35% 1511 35.00 51.58% 53 Danny Manning Saint Joseph's 46.67% 1504 43.83 50.61% 54 Casey McAdam Lehigh 51.65% 1503 47.65 50.39% 55 Brandon Bull Canisius 46.81% 1502 46.40 50.30% 56 Frankie Kelly UNC 51.09% 1500 49.79 50.06% 57 Mario Carrera St. John's 43.81% 1498 35.81 49.75% 58 Nick Haley Mt. St. Mary's 48.88% 1496 43.47 49.35% 59 Andrew Muscara Vermont 50.70% 1494 39.48 49.14% 60 Brad Lott Michigan 52.79% 1490 36.99 48.54% 61 Artie Marrapese Albany 37.17% 1485 34.21 47.81% 62 Adam Broeckaert Lafayette 42.78% 1481 39.90 47.28% 63 Ryan Shaw Providence 42.31% 1480 36.74 47.11% 64 R.G. Keenan UNC 40.63% 1477 41.78 46.66% 65 Will Abbott Saint Joseph's 49.62% 1473 42.48 46.16% 66 Phil Hession Dartmouth 42.75% 1470 36.39 45.76% 67 Cullen Cassidy Marquette 44.83% 1468 45.08 45.40% 68 Bryan Price Air Force 47.20% 1461 34.93 44.47% 69 Damien Hicks Detroit 44.04% 1461 41.22 44.38% 70 Conor Pequigney Towson 39.51% 1454 41.92 43.38% 71 Mark MacDonald Sacred Heart 42.28% 1450 39.25 42.87% 72 Peter Moran Richmond 41.79% 1445 34.55 42.19% 73 Mitch Wilson VMI 46.45% 1445 37.35 42.14% 74 Stephen Soriano Bellarmine 41.29% 1437 41.67 40.98% 75 Louis DiGiacomo Fairfield 43.33% 1432 45.79 40.33% 76 Anthony Labetti Wagner 45.90% 1426 42.94 39.54% 77 Joe Kemp Sacred Heart 45.51% 1424 44.91 39.26% 78 Nicholas Beaudoin Robert Morris 39.39% 1417 45.97 38.30% 79 Brett Johnson Manhattan 39.60% 1414 41.00 37.88% 80 Chris Barney Robert Morris 42.86% 1403 43.13 36.37% 81 Sam Rosengarden Jacksonville 42.86% 1383 40.84 33.79% 82 Jet Harding Jacksonville 36.13% 1381 45.05 33.55% 83 Duncan Campbell Monmouth 32.88% 1362 42.88 31.13% 84 Marco Mosleh Monmouth 30.85% 1353 49.55 30.02%

Up Next: Examining the Results

Chris Hampton

What do the following face-off guys have in common? Air Force’s Erik Smith, Duke’s Brendan Fowler, Marist’s Dominic Montemurro, Canicius’s Tim Edwards, Notre Dame’s Liam O’Connor, Penn State’s Drake Kreinz, Rutger’s Joe Nardella, Georgetown’s Tyler Knarr, Villanova’s Thomas Croonquist, Marquette’s Paul Riportella, UNC’s Frankie Kelly, and Drexel’s Nick Saputo.

If you answered that they are rated 1500 or better, you would be correct. More importantly, they’re all players that Denver’s Chris Hampton had to face-off against during the 2014 season and accounted for 218 of the 310 face-offs he took.

Thanks to postseason tournament rematches of regular season games he went up against Fowler and Nardella twice and thus between Fowler, Nardella, Knarr, and Saputo, nearly 40% of the face-offs he took during the 2014 season were against 2014 All-Americans and 2015 preseason All-Americans.

Even the below-average opponents he faced off against weren’t that far below average; players like St. John’s Mario Carrera and Providence’s Ryan Shaw.

Hampton ran a gauntlet of top face-off opponents at a 52.4% clip in 2014 and the model loves him for it. Despite the middling win percentage, he is without a doubt a top 10 returning face-off guy and yet has remained completely under the radar. With the graduations of Knarr and Croonquist along with Rutgers and Nardella leaving for the Big 10, he is the best returning face-off man in the Big East and very deserving of his preseason All-Big East selection.

Kevin Massa

Any reasonable rating system was going to have Massa near the top. However, 2014 postseason awards and 2015 preseason predictions have him as the best face-off guy in the country or at least in a top tier of two along with Maryland’s Charlie Raffa.

However, the ratings indicate that Raffa is clearly head and shoulders above Massa and that Massa is better viewed as being at the head of the pack of second tier guys chasing after Raffa.

By either winning percentage or my ratings, the four best players that Massa faced off against in 2014 were, in order, Raffa, Drexel’s Nick Saputo, Colgate’s Alex Kinnealey and Siena’s Casey Dowd. Massa didn’t finish above 50% vs. any of them, going 5-18, 8-22, 7-15 and 10-20 respectively.

Massa had good days against some good face-off guys, going 14-21 against Yale’s Dylan Levings, 14-23 against Syracuse’s Chris Daddio, 10-16 against Hartford’s Adam Yee and 21-26 and 10-13 in two games against Hobart’s Jake Shapiro.

However, his Division I- leading 68.8% win percentage was inflated on the strength of some very good days against some not-so-good opponents. He was 9-for-9 against Richmond’s Jackson Cabot, who finished the year 8-49, as part of a perfect 19-for-19 day against the Spiders.

Teams also resorted to using unconventional players against him. For example, Wagner had defender Matt Bunting take the only 17 draws he would take all season against Massa, only managing to win two of them.

Don’t get me wrong — Massa is still an elite face-off guy, but there isn’t the separation between he and the other top-but-not-Raffa players that his win percentage seems to indicate.

Up Next: Improvements and Future Research

Improvements and Future Research

Probably the most obvious area for addition research into face-offs is attempting to account for the contributions of wing players. However, there are several difficulties with any attempt to incorporate wing players into a model that I think preclude it from being the most promising area.

First, there is no readily available source of information on which players are on the wings for any given face-off. The only way to know for sure is when a wing player picks up the groundball after a face-off, they are credited with the groundball in the play by play. Even that isn’t perfect because on occasion a groundball might end up in the restricted area and thus it could be picked up by an attackman or defender who didn’t run in from the wing.

Second, the ability of face-off men to win it to themselves means that not all face-offs involve the wing play. Further, a groundball picked up by the face-off guy doesn’t always indicate a clean win and a groundball picked up by a wing player doesn’t always indicate that it was not a clean win. The tendency of some face-off specialists to win it to themselves further complicates the situation by skewing those correlations in what is likely an extremely biased fashion.

At some point in the future, perhaps this could be fixed by the recording of an additional stat for “clean wins,” which would designate face-off wins where the ball was possessed by one of the players taking the face-off before any of the wing players could become involved.

Third, teams that nearly always use the same players on the wings will create cases in which it becomes nearly impossible to meaningfully distinguish between the performances of the player taking the face-off and the wing player or the two players on the wings because they will always be on the field together.

In terms of assessing the importance of wing play, the most promising way to do it might be to look only at face-offs taken while one team has a player serving a penalty. Whatever the differences the talents of various wing players make in determining the outcome of a face-off, there is little doubt that it is better to have two of them while your opponent only has one.

The aspect of face-offs that I would guess to be the most promising is face-off violations. They are reported in both box scores and play by plays, so it would not be difficult to learn which player violated and on which face-off it occurred. A player who moves early or commits some other violation is credited with a loss and their opponent with a win. However, that loss and win are different and perhaps that difference is enough to be worth creating a greater distinction.

If players are equally likely to commit violations regardless of their opponent then credit for a win should not be given to the opponent of a player that commits a violation. Perhaps players are more likely to commit violations against better opponents and less likely to commit them against inferior opponents, or maybe that effect requires a certain minimum threshold to begin to make an impact. Perhaps it depends more on how the face-off results were going that particular day, rather than the players’ ratings.

Box scores also report the referees for each game, which makes it possible to consider the possibility that certain referees are more likely to call or cause violations than others.

The Ratings in 2015

The plan is to publish updated ratings on InsideLacrosse.com every week throughout the 2015 season (starting next week) after each ratings period ends. The final ratings from the 2014 season will be the starting ratings for the 2015 season. Each week, I also plan to highlight some important results from the previous week that made an impact in the ratings and a matchup to watch during during the next week.

This week’s matchup to watch is one that nobody is talking about because people don’t realize that Alex Kinnealey is easily a top 10 returning face-off specialist and got the better of Kevin Massa last year by winning 8 of 15 draws. The two will be back at it this Saturday when Colgate visits Bryant.