Numbers Never Lie is a sports show featured on ESPN 2 where the presenters conduct statistical analysis in order to indicate a “truth” about an athletic performance, an athlete’s comparative value, or perhaps athletic dominance over time. The implication is that the truth may not be obvious to the casual or even the die-hard sports fan, whose qualitative or otherwise non-numerical analyses are insufficient to determine the true value of a player. Statistics, the show claims, will answer a query definitively. The very premise of Numbers Never Lie mirrors the obsession that we have in the United States with using numbers to prove our points.
Returning to my analogy of the study of sports and baseball, it is clear that numbers can deceive, or at least can be used to indicate a “truth” which is in fact a “falsehood.” Numbers may “never lie” of themselves, but, as is the case with all things, neither can they speak in a vacuum. When numbers are forced to speak in an arena where there is incomplete information, we cannot be sure that the numbers are telling the truth. Those sneaky numbers! How can we trust them? Well, we simply need enough background information to give us the necessary context.
We see this in baseball. Let me take you back to 1998 and 1999. It was in the middle of what has become known as the “Steroid Era.” The Houston Astros played in the NL Central and were actually good, winning it both years. The Cubs still sucked, especially in ‘99. But the real thing to remember from those two years was the homerun race between the St. Louis Cardinals’ Mark McGuire and the Chicago Cubs’ Sammy Sosa. I was a kid who got drawn into baseball as a result of the race. In 1999, after the season when McGuire jacked 70 homers, smashing Roger Maris’ record, my pops took me to go see the Cubs and Cards play, my first baseball game ever! Though the game got rained out after six innings, I got to see both McGuire and Sosa smash homeruns, making my day a good one.
But, in retrospect, though I know I saw McGuire and Sosa homer, and I know they hit 65 and 63 homers that year respectively, I have to reconsider the numbers in light of their tainted legacy. Would they have been able to hit those bombs had they not been all ‘roided up? Was what I saw just a farce? Should those homeruns have counted even though they had cheated? As a kid, I would have had no idea. Those numbers meant those fellas were great at hitting the long ball. Their feats made them sure-fire Hall of Famers… Right?
The numbers are what they are, but many Hall of Fame voters believe that they give a misleading picture. Many voters have refused to vote for Hall of Fame candidates associated with steroids and this has the effect of rendering those players’ career statistics as illegitimately achieved. The Hall of Fame voters took those numbers and considered them within the context of what they knew. This is an approach that often cannot be said of politicians, who frequently refuse to seek out the information that would provide the background to the numbers they cite or even directly contradict their findings. Two notable examples are the 17-year ban on gun violence research at the CDC, and the rejection of the recommendation to decriminalize marijuana which came from the federally commissioned Shafer Commission report. Politicians are eager to cite raw numbers when it suits them, and are the quickest to provide the background that might change the way we think about those numbers when it doesn’t. But talking politics is a bit of a faux pas, and we all know that sports is a go-to conversation topic, so let’s return to numbers in baseball and we will have something safe to talk about at the water cooler.
Current ESPN baseball commentator and legendary pitcher Curt Schilling claimed that steroids and human growth hormone (HGH) “make bad players good. They make good players great. They make great players Hall of Famers.” How very European of Mr. Schilling to use qualitative means to come to an answer; usually Americans would demand numbers! Now, I am not suggesting that Schilling’s observation be disregarded (though it should well be questioned, as we shall do later) — coming from the European tradition myself. What I would point out that it is such an observation, made by an expert analyst who knows the game intimately, provides a context or a frame that changes the way we evaluate the existing data sets. If we consider the statistics of Hall of Famer baseball players in the Steroids Era, we look at them differently if we take seriously Schilling’s claim that steroids make you a better baseball player. Again, I am not suggesting that we should do away with numbers, I simply propose that such observations change the way that we understand numbers, and therefore change the truth that those numbers seem to justify or legitimate.
Schilling’s statement also raises several questions which beg definitive answers, questions such as: “If baseball had always been clean, what records would still have fallen?”; “How many players actually juiced?”; and “To what extent did steroids actually help player X break record Y?” may well be unanswerable for one reason or another.
But let us briefly focus on this last question: “To what extent did steroids actually help player X break record Y?” The question, admittedly, is a very complicated one. It requires us to know several things which may well be impossible to know. For instance, in order to answer the question, we would have to establish the extent to which steroids assisted player X in the skills and attributes which are necessary to break records Y, (it’s no good saying steroids helped a player break a record if we can’t demonstrate they had a positive effect and what that effect was). Secondly, we would have to establish the extent to which the benefits from steroids were counteracted by potential detriments, (such as the fact they make your berries small, lower your self-esteem, and therefore make you run slower –I’m joking but you get the idea). Thirdly we would have to evaluate to what extent the benefits from steroids were neutralized by the benefits gained by an opposing player who also used steroids. If a batter and a pitcher both used steroids, is it then a “fair” match up, thus rendering the statistics valid irrespective of the steroid use? We lack the data to even to begin to answer these questions. Does that mean that we rely on our gut instinct to answer these questions? Not if we wish to be systematic and responsible investigators. We demand data in order to develop a degree of confidence. If that data is unavailable, then we qualify our findings.
When we fail to qualify findings and observations appropriately, fact and opinion are often confused. This occurs especially when discussing topics that evoke emotional responses, like cheating in baseball (or sports in general).
Let us qualify Schilling’s observation with numbers and context that we agree to be true. If we look at the MLB record books, we see that the hitting records that have come under the most scrutiny are McGuire’s eclipsing of Roger Maris’ 61 home runs, and Barry Bond’s eclipsing of McGuire. Hitting records such as Hugh Duffy’s .440 batting average in 1894, Chief Wilson’s 36 triples in 1912, Earl Webb’s 67 doubles in 1931, Di Maggio’s 56-game hit streak in 1941, and Rickey Henderson’s 130 stolen bases in 1982 have not been touched. Context is important for some of these records, given the dimensions of Wilson’s home park, Forbes Field. But despite all of this, with the exception of Henderson’s stolen bases, none of the records were set in the Steroid Era.
Pitching records, perhaps, are ambiguous. On the one hand, Roger Clemens, who has been accused of juicing, holds the AL career and single game strikeout record. On the other hand, several records are held by players who have never been linked to steroids or who set their records before the steroid era. The history of unchallenged records in baseball could suggest that steroids do not, in fact, necessarily help players break all kinds of records, and this is especially the case with those related to speed and to accuracy. The history of unchallenged records in baseball certainly does not support Schillings assertion that steroids make good players great, because if they did, why didn’t more records fall?
The numbers I have cited likewise do not demonstrate anything based on Schilling’s assertion that steroids and HGH “make bad players good and good players great.” I would also question the part of Schilling’s claim that chemicals can make bad players good. It doesn’t make any sense to say that having bigger and stronger muscles, one characteristic important for success in the game, would magically improve coordination, which is equally important, for example. I, for one, really suck at hitting a baseball. When I was a kid and I used to take part in the annual softball game. They had a rule for kids like me that allowed us to be pitched enough balls so that we could put one in play; at least we had the fun of running to first base (twenty-seven strikes later)! And would using steroids make me smack that ball into play any more so? I doubt it. However, if we could definitively prove Schillings’ statement to be overstated, it could provide a sufficient disincentive to cheat given the nasty side effects.
In any event, what is clear is that we must be careful with both qualitative and quantitative data. Ideally they are presented together in order to support and complement one another. When this happens, the audience is better able to understand exactly what numbers mean. Without enough information however, there are several questions that need to be addressed, even if the only way to do so is to recognize that we do not know or are quite unsure of the answers. Whether in politics, in baseball, or in any field where they use numbers or statistics to justify their claims, perhaps we should always be slightly suspicious, slightly skeptical, slightly critical of what they are claimed to represent.
So in the end, do numbers ever lie? Perhaps not, but they do not always speak the truth.
NB: all baseball records were checked with the Baseball Almanac.