Baseball - Understanding Statistics Part Three

As previously discussed, the ERA, WHIP and BAA form the most basic of pitching statistics when analysing pitchers' abilities. In this article, we discuss the WHIP and BAA.

WHIP (Walks and Hits Allowed per innings)

We use the WHIP- Walks and Hits allowed per inning- as a secondary statistics to gauge the ease or difficulty a pitcher has in obtaining his ERA and to gauge his command ability. The standard is 1.00-1.30. For example, if a pitcher has an ERA of 2.57 but a WHIP of 1.95, we can deduce he was fortunate to escape without further damage. We can also deduce he has trouble with his command.

For some handicappers, the WHIP is a better indicator of what a pitcher has done as they believe it indicates a pitcher’s true ability. The numbers of hits and walks allowed is not definitive in the statistics and a glance at his actual walks and hits totals will clarify his command issues. For example, if a pitcher has an ERA of 4.40 but a WHIP of 1.04, what can we deduce from that pitching line? We can either take the view the pitcher had good command of his pitches but is prone to giving up a home run or we can deduce that he had a good outing apart from one bad inning. The Box Score will tell us whether that is true or not.

BAA (Batting Average Against) or OBA (Opposition Batting Average)

The third basic statistic is the BAA or the OBA, which is the rate opposing batters are hitting the pitcher. Whilst the batting average benchmark of .300 is considered good, the pitcher BAA benchmark is around .250 and under. This tells us exactly what is designed to do. For example if a pitcher’s BAA for the season is .250, that only tells us the opposition is hitting him at a 25% clip: One hit for every four batters faced. Considering there are three outs in an inning and if there are no walks that equates to a perfect 1.00 WHIP. Combining the WHIP and the BAA is a useful tool as it tells us the probability of a pitcher giving up hits.

Combined with other variables such as night/day and home/road splits, we can begin to form a clear picture of a pitcher’s ability in every game situation and combined with the hitting team’s statistics, we can envision how a game can pan out. Of course, the numbers may not play to form, but whilst the numbers do not lie, the statistics are only the mean. A pitcher may give up six runs in six innings in his first start then proceed to pitch shutout games in his next two starts in fourteen innings for a 2.70 ERA in 3 games. The 2.70 ERA does not tell us he will give up around 2-3 runs every seven innings. His ERA will have rise and falls in performance.

We also combine other factors such as weather conditions, umpire bias, bullpen competence and getaway days (Sundays and Thursdays are considered getaway days as the team may travel to a road game the next day and may opt to give playing time to bench players, thus fielding a weakened offence) to make a final decision on a bet.

But the problem in solely using the three basic statistics is that they do not tell us about the pitcher’s true ability hence the development of sabermetrics. The three statistics does not tell us how he obtained those numbers. A pitcher may give up five runs in five innings for a 9.00 ERA but the 9.00 ERA didn’t tell us, he may just had one bad inning and improved as the game went on, or he was unlucky the groundballs and line drives he gave up found gaps in the infield. Like the film, Sliding Doors, baseball is full of questions: what if? We only have the general idea of what a pitcher has done. We don’t know exactly how he actually achieved those numbers. What if the middle infield had missed turning over a double play? (That kind of situations are not recorded as an error) What if the pitcher had thrown a strike on a full count instead of walking the batter? What if a pitcher has simply been very unlucky or lucky? What if a pitcher was victimised by bad defence?

The point is that in baseball; literally anything can happen in any situation with a swing of the bat or a badly located pitch. What if? So many possible effects on any given situation: a 2-2 count with bases empty, a groundball that found a gap in the infield and so many possible outcomes. Whilst that may seem daunting, baseball’s obsession with sabermetrics is about finding out who has real talent, who has been lucky, and basically stripping everything a pitcher has done in its rawest form. It is not an exact science, but an art: the art of projecting a baseball player’s performance, the art of finding undervalued players.

Most sabermetrics can be found on sites such as the Hardball Times and Fangraphs and they have advanced statistics. Below is a list of advanced statistics that can be found on those sites.

BABIP (Batting Average on Balls in play)
FIP (Fielding Independent Pitching)
xFIP (Expected Fielding Independent Pitching)
FIP-ERA (Fielding Independent Pitching MINUS Earned Run Average)

Those are general but advanced statistics that summarizes a pitcher’s performance. There are many more in-depth statistics such as walks per nine innings (BB/9), strikeouts per nine innings (K/9), Line Drive percentage, Ground ball percentage, etc. that simply breaks down the pitcher’s performance into its rawest form. Many of those are self-explanatory but have very good functionality. For example, a look at a pitcher’s career K/9 rate as a sole statistic can lead to clear evidence he has declined in terms of making quality pitches and as a consequence, struggles to strike out batters. It is a good idea to familiarise yourselves with those statistics. In the next article, we will explore BABIP in depth.

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