Pitch Quantification Part 1: Between Pitcher Comparisons of QOP with Conventional Statistics" (2016)

Biola University Digital Commons @ Biola Faculty Articles & Research 2016 Pitch quantification arP t 1: between pitcher comparisons of QOP with conventional statistics Jason Wilson Biola University Follow this and additional works at: https://digitalcommons.biola.edu/faculty-articles Part of the Sports Studies Commons, and the Statistics and Probability Commons Recommended Citation Wilson, Jason, "Pitch quantification Part 1: between pitcher comparisons of QOP with conventional statistics" (2016). Faculty Articles & Research. 393. https://digitalcommons.biola.edu/faculty-articles/393 This Article is brought to you for free and open access by Digital Commons @ Biola. It has been accepted for inclusion in Faculty Articles & Research by an authorized administrator of Digital Commons @ Biola. For more information, please contact [email protected]. | 1 Pitch Quantification Part 1: Between-Pitcher Comparisons of QOP with Conventional Statistics Jason Wilson1,2 1. Introduction The Quality of Pitch (QOP) statistic uses PITCHf/x data to extract the trajectory, location, and speed from a single pitch and is mapped onto a -10 to 10 scale. A value of 5 or higher represents a quality MLB pitch. In March 2015 we presented an LA Dodgers case study at the SABR Analytics conference using QOP that included the following results1: 1. Clayton Kershaw’s no hitter on June 18, 2014 vs. Colorado had an objectively better pitching performance than Josh Beckett’s no hitter on May 25th vs. Philadelphia. 2. Josh Beckett’s 2014 injury followed a statistically significant decline in his QOP that was not accompanied by a significant decline in MPH. These, and the others made in the presentation, are big claims. Can they be substantiated? Is QOP a valid statistic? Could it be a reliable new stream of information about pitch quality? The purpose of this paper is to provide evidence to MLB analysts, analyst-types, and fans of the affirmative. We originally developed QOP with the desire to provide a more comprehensive measurement for individual pitches that could be used by pitching coaches for player development and by scouts to identify player potential. The analytical and medical applications in our presentation came later. Before we can go about the business of developing QOP for such high-level applications, however, the MLB analysts with whom we have spoken are asking for evidence of the validity of QOP. How does it compare with conventional statistics like ERA and WHIP and FIP, or BABIP and OPS? Other results? Is it consistent? Is it better? This paper presents that comparison. I will make a case that while QOP values can be shown to associate with traditional pitch classification at a macro level, direct comparisons of QOP with conventional statistics have low correlation due to the difference between the statistics and the loss of variation inherent in averaging hundreds of pitching and hitting events. However, when QOP is compared with raw results data -- number of bases earned per hit -- and conditioning by pitch designation, it is shown to strongly associate with results. Additionally, I demonstrate a positive way to relate QOP to ERA is as a predictor. QOP has a 76 to 97% success rate for predicting a pitchers’ drop in ERA or FIP in subsequent seasons for pitchers with QOP average over 5.0. In order to be as clear as possible at the outset, let me remark on what QOP is, and is not. My colleagues and I have designed the QOP function to calculate the three key properties of a pitch which 1 Biola University, 13800 La Mirada, CA 90639; [email protected] 2 © Greiner Agencies Inc., 1478 Welbourn Drive, Edmonton, Alberta, Canada, T6M 2M1 | 2 are: trajectory, location, and speed. Specifically, these properties are determined from the following components, shown with their relationship to QOP: 1. Increased rise lower QOP (for curveballs) 2. Increased total break higher QOP 3. Increased late vertical break higher QOP 4. Increased horizontal break higher QOP 5. Closeness to corners of strike zone higher QOP 6. Increased velocity higher QOP Thus, the calculation of QOP is completely objective because it incorporates the above objectively measurable pitch components. There are no subjective evaluations in these calculations2. As the old English measurement system had inconsistent lengths between the “foot” measurement of different merchants, so contemporary major league baseball has inconsistent assessments of pitch quality between observers. As the “foot” inconsistency was resolved by standardized ruler, so we would like QOP to become a “ruler” of pitch quality for baseball. Therefore, the QOP model does not include pitch tempo, pitch sequence, or pitch intention. Neither do we consider pitcher deception or batter anticipation to be measurable properties for inclusion in QOP. We include only measurable components in the QOP “ruler”. In our theory of pitch quantification, we distinguish between pitch quality (QOP), pitch context (count, tempo, sequence, base runners, etc.), and pitch effectiveness (results). Pitch quality is the foundational issue. To the degree that the measurement of pitch quality is flawed, the estimation of batter effects and prediction of results will be flawed. We hypothesize that when accurate pitch quality is correctly combined with pitch context, then pitch effectiveness/results can be accurately modeled. QOP is our attempt to measure pitch quality accurately. In this paper, we are not addressing the relationship between pitch quality and pitch context. Such development of our theory remains for future work. In order to get there, the prior issue is the establishment within the baseball analytics community of the validity of QOP, which is the focus of this paper. Once QOP, an accurate measurement of pitch quality, is established as a valid metric, future development can occur. 2. Overview of QOP I begin with a brief description of how QOP is calculated and how it differs from conventional statistics. Those already familiar with QOP may skip this section. The formula for QOP is a multiple regression model with the following form (see Figure 1 for a graphical representation of components): QOP = β1*rise + β2*break.point + β3*vert.break + β4*horiz.break + β5*location + β6*MPH where 1. rise: the number of feet the ball rises vertically to the maximum height 2. break.point: the horizontal distance (in feet) from the release point to the maximum height 3. vert.break: the number of vertical feet from the maximum ball height to the point it crosses the plate | 3 4. horiz.break: the number of horizontal feet, at home plate, from the “straight line” crossing point of the ball to the actual crossing point 5. location: a measurement of how easy the pitch is to hit, on average, based upon the location in the strike zone (the higher the value, the easier to hit, see Figure 2) 6. MPH: pitch speed Figure 1. Curveball trajectory diagram. The trajectory components of a pitch used to develop the initial QOP model. In the model shown, the parameters were derived from the pitch measurements using a low-tech method. The current model uses PITCHf/x data. # Residuals: # Min 1Q Median 3Q Max # -2.1066 -0.4282 -0.0656 0.3274 3.3782 # # Coefficients: # Std. Error t value Pr(>|t|) # rise 0.69040 -2.809 0.00519 ** # break.point 0.01837 12.956 < 2e-16 *** # vert.break 0.01964 50.342 < 2e-16 *** # loc 0.01537 -65.053 < 2e-16 *** # horiz.break 0.08130 4.050 6.1e-05 *** # --- # Residual standard error: 0.6724 on 419 degrees of freedom # Multiple R-squared: 0.9578, Adjusted R-squared: 0.9573 # F-statistic: 1903 on 5 and 419 DF, p-value: < 2.2e-16 Table 1. Statistical software output for QOP model. It may be noticed that MPH does not appear in the output. This is because we combine this trajectory and location parameter model with MPH separately. The rise and location coefficients are negative, while the break.point, vert.break, horiz.break, and MPH are all positive. This makes sense, because a rising pitch and bad location make for a low quality pitch whereas a big break and fast speed make for a good quality pitch. Under extensive model testing, there were no quadratic relationships or interactions found. It is noteworthy that | 4 this is a zero intercept model. The model fit is excellent with a p-value < 10 for all coefficients (except rise, which is 0.005), and adjusted R2 = 0.957. All QOP work has been−5 done in R3 and the model output is in Table 1. Unfortunately, the patent pending model is proprietary and the coefficients cannot be released at this time. Figure 2. Diagram used for developing the location variable in the current QOP model. Different colors indicate different points assigned to the location parameter. The best location is near the strike zone corners, and near the edge of the strike zone. The worst location is the exact center of the strike zone, and far out of the strike zone. The edge of the strike zone, inside and out, rates well because it can lure the batter into swinging, while still difficult to hit. This is a conceptual diagram only; this is NOT the exact way location is calculated for the QOP model. 3. Results How does QOP compare with pitching statistics like FIP and batting statistics like OPS? These questions form the subject of the first two sub-sections, below. Since QOP provides a number for every pitch, we can look at either an average or the individual pitch values. To distinguish these, we let QOPV refer to a set of individual pitch values and QOPA refer to their average. While QOPV is normally on a -10 to 10 scale, there are no theoretical limits to the endpoints, since it is a regression model.

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