How Much Does the Umpire Affect the Game?
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How Much Does the Umpire Affect the Game? Look again. Not much. Willie Runquist 1 the 1991 issue of the Baseball Research Journal, vidual components differ from game to game for a par.. Richard Kitchin presented data from which he con.. ticular umpire. eluded that the differe11ces il1 ulllpires TnHy hAV~ Thr data for this C111alysis consists of tIle result8 of840 substantial effects 011 tIle results ofgames. Certainly no An1erica11 League garnes itl 1991, 30 for each of 28 urn.. one would argue that a specific "bad" call could not in.. pires. If an umpire worked more than 30 games behind fluence a game's outcome, or that in a specific game the the plate, the games were selected at random. The most umpire's calling ofclose plays has no effect, but Kitchin's games omitted for anyone umpire was seven. Umpires accusations are more serious because he seems to indi.. who worked fewer than 30 games were not included in cate that different umpires have various systematic biases the sample. in their calls, that produce (1) more home or visiting For each game, I recorded at bats, runs, hits, doubles, team wins thall.onewould ~xpect, and (2) more orless triples,home runs, walks and strikeouts for both the vis .. offensive action. iting team and home team. The results ofthese selected It would be surprising if umpires did not differ in their games ·were very close to league averages based on all judgments. Individual differences in human judgment 1134 games. The averages are shown in the table at the were the subject ofpsychological inquiry long before the top of the next page as the mean number of events per ittV~Iltlorlofba.seball, 8.L1J Jifferences pervAde every hu .. game in each category. man activity in which such judgment takes place. Among baseball analysts, omitting available data is Despite the considerable training designed to produce somewhat heretical. However, there are distinct statisti... uniformity, umpires' judgments should be no exception. cal advantages in equating the number of games per T,he question is not \vhether such differences exist, but umpire, and th.e procedure slloulJ lUive nu effect on the what effect they have on the game. conclusions. The above table establishes the fact that the Unfortunately, Kitchin's data do not allow a clear con.. sample ofgames we used is representative ofleague play clusion in this respect. He presents only totals and as a whole. averages for various umpires,assuming that these values are stable indicators ofan umpire's bias. Since an average Differences in Home/Road Wins and Losses results from the combined effect of several games in Kitchin's data showed what appeared to be large which the performance of the teams varies, averages differences in the percentage of games that are won by must be evaluated in relation to how much the indi.. the home team, depending on which umpires were work.. ing the games. He that this meant that some may team more In Willie Runquist is a retired research psychologist living in Union Bay, DC, coming to this conclusion, Kitchin failed to consider the Canada. ----~0~---- THE BASEBALL RESEARCH JOURNAL Table 1 AB RH 2B 3B HR BB so BA SA OB League 68.4 8.97 17.81 3.25 .40 .72 6.81 11.41 .260 .395 .329 Sample 68.5 9.04 17.90 3.28 .40 .70 6.89 11.30 .261 .395 .329 fact that large deviations from the league average may overall agreement in the ranking of umpires on home occur simply by chance. Therefore, it is necessary to team winning percentage. The correlation coefficient compare his variations from the league average with between the ranks for the 23 umpires who appeared in those that would naturally occur if there were no differ, both sets of data was virtually zero (r = .048). The um, ences among umpires. pire that had the largest home bias on Kitchin's list We can do this with a simple statistic called Chi,square, ranked seventeenth in 1991. which is based on the discrepancy between the frequen, The standard error ofa statistic gives us a direct mea, cies of home team and visiting team wins and losses for sure of the expected deviation from from the overall each umpire and those expected on the basis ofchance. average of .518 .The standard error for the proportion of The obtained differences, and hence the size of Chi, home team wins in a sample of 30 games is .091. We square, must be larger than expected by chance if we are know that 95 percent ofthe values for individual umpires to conclude that umpires differ systematically in home should fall within two standard errors of the overall pro, team bias. In the case of Kitchin's data, the Chi,square portion (that is, somewhere between two standard errors for neither American League umpires, nor for National less than .518-.336-and two standard errors more than League umpires is large enough to justify this conclusion. .518-.700). All 28 umpires were within this range. The For the National League, the Chi,square was 26.86, largest proportion of home team wins was .667 and the tilHl rUl Llle AluelicHll LeHgue iL ~\lH~ 34.91. Clli.. ~quHle~ sluallesL ~va~ .367. CUllClu~iull? TIle JiiTelellces ueLweell as large as the National League value will occur by umpires are no greater than you would expect by chance. chance about one time in three, and ones as large as the In short, particular umpires appear to have very little pre, American League/value about one time in six. Statisti, dictable effect on whether the home team wins or loses. cally, the burden ofprooflies with those who claim that more than just chance is involved. These values are Do Hitters' and Pitchers' Umpires Exist ?-Remember, much too likely to be chance occurrences to give us a the real issue is not whether differences in umpires exist, strong argument for any bias. but whether they produce any measurable effect on the My·data from the American League umpires in 1991 outcome ofgames. The correlation between umpire bat, show even greater uniformity. The number ofhome team ting average in my sample and in Kitchin's sample was wins for each umpire is shown in 'Table 2. 'T'he Chi, virtually zero (r = .069) . Very simply, players' composite square was 19.62. Values this large will occur about four batting average with a particular umpire behind the plate times out offive by chance. in 1991 was not predictable from the batting average in tIle gallles lle worked during the previous six years. Table 2 Since Kitchin does not'present the complete data for Frequency of Home Team Wins other measures, comparisons of this type call1lot be for 1991 AL Umpires in 30 Games. made, but complete data for the 1989 season is available in 'The Baseball Scoreboard published by Stats Inc. Values Wins Umps Wins Umps for batting and slugging averages, as well as for runs, 20 1 15 5 walks, and strikeouts per game are given for all 28 urn, 19 o 14 3 pires in our sample. The correlation coefficients between 18 6 13 2 1989 and 1991 performance for each statistic are listed 17 5 12 3 in Table 3. 16 2 11 1 All but the correlation for bases on balls are small but positive. The square ofthe correlation coefficient indexes Overall Mean: 15.5 (.518) SD = 2.33 the year,to,year consistency in the average amount of offense generated in a particular umpire's games. I also compared the results for individual umpires in values are all less than .06, which means that the year"'to this sample with those in Kitchins' data. There was little year consistency is less than 6 percent. THE BASEBALL RESEARCH JOURNAL Table 3 Table 5 Correlations on Various Measures of Mean Number ofVarious Events for Each Umpire Umpire Performance Between 1989 and 1991. R H 2B 3B HR BB SO Batting Average .175 1 11.27 19.53 3.53 .37 2.27 6.87 11.57 Slugging Average .242 2 10.67 19.23 3.93 .37 1.87 7.67 11.17 Runs per Game .202 3 10.20 20.10 3.40 .47 2.20 6.87 11.37 Walks per Game ...084 4 10.03 18.73 3.80 .40 1.67 7.43 11.90 Strikeouts per Game .242 5 9.97 18.67 2.87 .37 1.90 6.47 11.37 6 9.77 18.50 3.10 .30 1.57 7.13 11.40 .Based on runs scored, which is arguably the most di.. 7 9.60 19.43 4.03 .43 1.77 7.53 11.48 rect measure of offensive activity, there is some 8 9.57 18.57 3.80 .43 1.37 7.70 11.73 consistency from year to year, but it is minimal. Of the 9 9.40 18.00 2.97 .40 1.83 6.37 11.57 seven "most offensive" umpires in 1992 (upper 25 per.. 10 9.37 18.03 3.47 .33 1.53 7.40 11.50 cent), only two of them were among the seven most 11 9.17 18.53 3.63 .40 1.43 6.67 11.80 offensive umpires in 1989. Of the. seven least offensive 12 8.97 18.33 3.67 .50 1.60 7.03 10.93 umpires in 1992, two were among the seven least offen.. 13 8.97 17.53 2.83 .50 1.53 7.53 11.80 sivein 1989.