IT COULD HAPPEN THE MAJOR LEAGUES IN 2012 A BASEBALL WIN In spring, baseball hopes spring eternal even though the odds are ACCOUNTING long for most teams. But sometimes the dreams come through. ANALYSIS What does it take to manufacture a miracle season? How realistic are these hopes for the 2012 major league teams? This book evaluates these hopes quantitatively using Baseball Win Accounting. Baseball Win Accounting provides an analytical framework for baseball using the on-base-plus-slugging (OPS) measure of performance for team offense and defense as the unit of account. It shows how hitting, pitching and even fielding can be evaluated using this consistent measure of performance, and how to aggregate player performance to team wins. IT COULD HAPPEN THE MAJOR LEAGUES IN 2012 A BASEBALL WIN ACCOUNTING ANALYSIS Dan Ciuriak April 2012 Copyright Dan Ciuriak, 2012. All Rights Reserved Foreword In baseball, hope springs eternal. Every year yields a new lineup for each team. The churn includes rookies, free agents and players moving into and out of the lineup, coming off injuries and so forth. It’s possible to dream and sometimes the dreams come through. The perennial problem is how to add up the impact of all the changes? This book tackles the adding up problem quantitatively using Baseball Win Accounting. Baseball generates a lot of raw statistics and baseball analysts have developed many more. Baseball Win Accounting doesn’t generate any new stats but rather it provides an innovative way to allow us to use powerful existing statistics, ones with which we’re already familiar, to better advantage. Baseball is a zero sum game: what a hitter achieves is at a pitcher’s expense. Evaluating a hitter in terms of one statistic (e.g., runs created, offensive winning percentage or the old garden variety triple crown stats) and the pitcher by another (ERA, WHIP or something more exotic) makes it impossible to draw direct comparisons. Moreover, many of these stats describe individual player performances in terms of team results, such as winning percentage. But players don’t win, teams do. Nor do players create runs – runs are the outcomes of sequences of events – they are team results. So to calculate an individual’s winning percentage or runs created is to create a confusion. To deal with the adding up problem in a consistent way, the contributions of hitters, pitchers and fielders have to be expressed in a common statistic that is both a meaningful measure of individual performance and also, at the team level, closely correlated with team success. This can be done by applying analytically the statistical information already at our disposal – this is what the Baseball Win Accounting framework does. The key to any accounting framework is to use a common basis for evaluation. Of the readily available statistics that are the same for both pitchers and hitters, by far the most meaningful is the sum of the on-base percentage and slugging average (OPS) for hitters and the corresponding on-base plus slugging percentage against (OPSA) for pitchers. Baseball Win Accounting develops a framework for applying these statistics in a consistent manner to evaluate team performance, both retrospectively and prospectively. The box below summarizes the elements; the rest of this book develops the underpinning for each of the relationships. The full framework is developed and discussed in Part II of this book. This is from the original 2001 edition of this book; an updated version is planned for next year’s book. 1. Team OPS = the average of the individual players’ OPSs, weighted by plate appearances. 2. Team OPSA = the average of the individual pitchers’ OPSAs, weighted by innings pitched. 3. One additional point on a team’s OPS translates into two additional runs scored over the course of a 162 game season. 4. One additional point on a team’s OPSA translates into two additional runs scored against over the course of a 162 game season. 5. When baseball is in a zone where games average nine runs per game, adding nine additional runs to a team’s margin of runs for and against over the course of a season adds on average one more win to the team’s total. Using this approach, this book dreams about the upcoming season in numbers. How realistic are hopes for the 2012 major league teams? How for real are the favourites? How long are the odds on the long shots? Is there any basis for nurturing a hope that a perennial cellar dweller might break out and manufacture a miracle season? Part I of this book looks at each of the major league teams and considers what it would take for the team to have a shot at the postseason—working within realistic performance bounds for individual players. A word about the projection method. The underlying methodology, Baseball Win Accounting, is developed in Part II of this book. The projections in Part I aren’t predictions, they are more in the nature of assessments of potential. Based on projected line-ups, assigned roles, depth charts and proven ability to deserve plate appearances and innings pitched, each player is given a guesstimated number of plate appears (PA) and each pitcher an innings pitched (IP) total. Their performance level is measured in terms of on-base plus slugging percentage (OPS) for hitter sand in terms of on-base plus slugging percentage against (OPSA) for pitchers. The beauty of this approach is that it is a zero sum game. Whatever hitters achieve is exactly equal to what the pitchers give up. Each team’s overall OPS and OPSA are equal to the average of the players/pitchers scores, weighted by their PAs and IPs. Calibrated to the current run environment, a team is projected to record runs for and runs against totals in line with their respective OPSs and OPSAs. In turn, the margin of runs for less runs against, divided by 9 added to 81 gives a team’s total projected wins. For player forecasts, I allow the numbers to speak: each player is given the average of the past five seasons, subject to adjustments if: (a) there has been an apparent distinct change in level of performance, (b) if there is a clear trend in the level of performance, (c) if the average is distorted by an exceptionally good or bad season out of line with the player’s overall minor and major league history, and so forth. For players with limited PAs or IPs in the majors, I take the relevant minor league average and discount it for change in level of play. I look at fantasy baseball projections and these appear to be based on the same principles. Because the results of the preceding season influence the projected lineups for the coming sense, there is a tendency for the projected lineups across all 30 major leagues to have a winning percentage greater than .500 and runs for to exceed runs against. Since the overall winning percentage for the league must be .500, and runs for must equal runs against, an adjustment is required. This is necessarily arbitrary. I bring the accounts into line by raising the OPSAs of pitchers by an amount needed to bring runs against into line with runs for. The adjustments are relatively small for any individual pitcher and do not affect the order of finish of the teams. The approach described above is obviously strongly influenced by mean reversion—that is, teams perform according to average levels of performance. By the same token, overall team outcomes tend to get bunched more towards .500 than is typical in a major league season. This is unavoidable and actually quite realistic as an expectation at the start of a season. Over the course of the season, the year-to-year variance in player performance takes over and shapes outcomes. Some players do better than average and some do worse, but the plus/minuses are not evenly balanced by team. Injuries are suffered and other shit happens. Some players have breakout seasons (sometimes apparently with a little help from the bottle). Some teams get lucky and others win fewer games than they appear to deserve on paper. At some point, some teams give up on the season and sell-off expensive players; other teams with a shot at the playoffs meanwhile do the opposite and spend money to lift their performance. All of these factors tend to create greater dispersion in the final results than are implied by the season-opening strategies Once the baseline projection is established, it is possible to ask the question of how much head room any particular team has. I approach this question in the following way. For an established player with a consistent record, there is a certain amount of variation in annual OPS and OPSA results. For example, Matt Kemp of the LA Dodgers has had the following OPS results and number of plate appearances over the last five seasons: Matt Kemp Team Position OPS PA 2007 Kemp, M LAD CF 0.894 311 2008 Kemp, M LAD CF 0.799 657 2009 Kemp, M LAD CF 0.842 667 2010 Kemp, M LAD CF 0.760 668 2011 Kemp, M LAD CF 0.986 689 Average 0.853 598 StDev .088 One way to express the amount of annual variation in the OPS is to calculate the standard deviation of the five OPS scores (StDev). This assumes that the OPS scores are generated by a random process which yields a given mean (the average OPS core of .853) and certain amount of variation around that mean that is characteristic of that process (in this case, the batter’s ability).