Americans love to use statistics to talk about sports, and when you dig deeper, sometimes it seems we use statistics without really knowing what they mean. The disparity between statistics and the actual game itself seems particularly relevant with the looming NCAA men’s basketball tournament and the billions of work hours lost scouring Web sites and stat lines looking for that edge.
In a way, statistics seem to be a way for fans to experience the game as a spectator more than a reflection of the experience of playing the game, or even actively watching it. Despite cultural aversions to simply become a number among the crowd, statistics boil down a player’s performance to several easily understandable numbers, which is exactly what many people fear. I would like to discuss how we can still use statistics, though, because they provide a way for people to take part in an event across distances of space and time.
The focal point for the article will be a statistic concerning the Virginia men’s basketball team: According to the latest Ratings Percentage Index, the Cavaliers have the nation’s No. 1 strength of schedule, which means they had the toughest schedule in the country. The honor and meaning of such a statistic seems debatable, depending on how we situate that statistic and the limits on what statistics can express.
Aside from the real implications — like how that number may or may not have mattered if the Cavaliers were anywhere nearer to the NCAA Tournament than the automatic bid for winning the ACC Tournament — the number seems to lose itself in satisfying our subjective wants as sports fans.
We could use this number to preview the team’s upcoming ACC Tournament first-round game against Boston College. Comparing the two team’s schedule strengths seems logical, but we run into our first roadblock: differing calculations. Both Rivals.com from Yahoo Sports! and rpiratings.com list Boston College as the 57th-ranked team in overall RPI, with a .5740 rating. However, the former lists the Eagles’ SOS as the 59th-toughest in the nation, while the latter rates their schedule the 71st. Let’s take a look at how a team’s strength of schedule is determined then.
Basically, a team’s strength of schedule can be determined by taking a fraction of its opponents’ win/loss records added with a smaller fraction of the team’s opponents’ opponents’ win/loss records. A team’s strength of schedule changes throughout the season as those percentages change.
A team’s strength of schedule imposes a descriptive sense of order that glosses over much of the mystery of the actual game. Here, we come to terms with statistics as terrible predictive measures; if the outcome of the game could be determined simply looking at how the team’s matched up on paper, then why play the game at all?
To bolster the use of the statistic in discussing sports, we next turn to the overall season’s trends, and the individual games that make up that SOS ratio. Virginia and BC do not share any non-conference opponents, and neither team boasts an impressive non-conference schedule. In eleven non-conference games Virginia lost all four of its games against top 64 RPI teams, and won its four games against teams below 175 in RPI rank, compiling a 6-5 record for non-conference games . BC split its two games against top-50 RPI teams and suffered only a loss against Harvard of the eight teams beyond the 175 mark, compiling a 12-3 record. So, we seem to be able to say: Virginia struggles when obviously overmatched and wins the games it should at home, and Boston College generally wins the games it should and can play better than we might think on occasion.
Games out of context, however, throw aside how the rhythms of the season affect performance in individual games — the game is the second-most important unit of analysis in basketball, with the first being the individual possession.
Looking first at Boston College, two of the team’s underachieving non-conference losses both came after the team won three or more games in a row. The second, at home against Harvard, came three days after BC upset North Carolina in Chapel Hill in the Eagles’ ACC opener, ending a thirteen-game winning streak. How can statistics explain two drastically different performances from the same team?
Putting these games in context gives a clearer picture, but we still miss how players are not simply archetypes of the five positions, and how different coaches use different systems to organize their players.
To use statistics, we could discuss how three guards scored 64 of BC’s 85 points against UNC, while the team spread the scoring around against Harvard. For Tyrese Rice, however, to take over the Harvard game like he did against UNC, he needed the whole game to establish a rhythm, rather than ratcheting up the tempo in the final minutes. Rice scored 11 of his 14 points against Harvard in the final four minutes. The impact of one player can change the result of a game, but it is difficult for one player to do that for an entire season. Also, we could mention the Eagles’ horrible three-point shooting — against a Harvard team that does not defend the three-point shot well — and an inability to get to the free throw line.
Beyond the numbers, we could argue that Boston College overlooked Harvard because of the emotional charge from beating then-top-ranked UNC, and when the team finally showed up, the game was too far out of reach.
Similarly, players that have not performed well throughout the season can step up, just as others drift back to the bench. Mamadi Diane’s performance against Maryland on March 10 in what may have been his second-to-last game as a Cavalier illustrates this: Diane posted 23 points on 7-of-12 shooting despite averaging only 5.3 points per game this season. These statistics of individual players seem to hit closer to the kind of substantive meaning of ‘truth’ the fan and writer searches for when using statistics, in part because the numbers for individual game performances can be correlated with specific plays.
What this discussion reaches for, ultimately, is one pattern that will explain how one team or player will play on a given night; however, that kind of statistic simply does not exist because change is constant and inevitable. The whole quilt of patterns provides a picture of a team, but the outcome of these static categories is by no means certain. These surprises keep us coming back to sports, and they give me something to write about.
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