Baseball - Understanding Statistics Part Two

Baseball may be technically a team game but the influence a pitcher can have transcends other factors. When we glance at the match-up, the first thing we note aside from the team is the listed pitcher.

Before we go on, it is important to emphasise that no single statistic defines a pitcher’s ability. We need a combination of statistical data to form a clearer picture. By understanding all the relevant statistics, we can generate, in our minds, the length and breadth of a pitcher’s true ability.

It is also important to note that the statistics represent the mean. With so many data available, we can make assumptions on whether regression to the mean is likely thus staying away from overvalued pitchers or possible improvement in his pitching, thus seeking out the value on undervalued pitchers. The statistics of any given pitcher may look good but they are generally averages and not a clear indication of how a pitcher is going to pitch in his next game. We also pay attention to the size of the sample. The bigger the sample, the easier it is to project a pitching line for any game.

For example, a pitcher may pitch with 0.00 ERA for the first three games of the season but that number is unsustainable as he would have to pitch scoreless outings for the whole season. Come the end of the season, his ERA will rise and for that ERA to rise, he will have a few games where he may give up five runs in five innings (9.00 ERA) causing his ERA to rise. With a sample of size of three games, it would be unwise to read too much into that on its own thus we collate the career data and split stats to form a better picture.

There are six main statistical variable I use when handicapping games. The ERA, WHIP, BAA (OBA), BABIP, FIP and xFIP. I start with the ERA, WHIP and BAA. Aside from the obvious win-loss record, the mainstream statistics used when gauging a pitcher’s abilities are the ERA and WHIP.

ERA (Earned Run Average)

The perceived primary mean of identifying a pitcher’s ability is through his ERA- Earned Run Average. The number is the ERA every nine innings. The formula is as followed:

Number of runs allowed                     X  9
Number of innings pitched

Therefore if a pitcher allows two runs in seven innings then his ERA is 2.57. The benchmark in terms of ERA is around 3.00.

What outcome can we gauge from the ERA?

On its own, the ERA can be misleading but is generally representative of a pitcher’s ability, whether it is over his career, or against a particular team. With so many statistical data representing different variables such as reflecting a particular ballpark performance or his home/road and day/night splits, as well as career, seasonal and monthly statistics, there is a wealth of information. How you, as a bettor interpret those statistics is the key to deciphering the games. There is a wealth of other statistics you can use to combine the ERA with to form a clearer picture of what to expect from your pitcher.

For example, if a pitcher has a career 3.56 ERA but in his current season, he is pitching to a 5.68 ERA, what assumptions can we make?

We can take the simplistic view that there is most likely further improvement in his ERA as the season goes on and choose to interpret his 5.68 ERA as an aberration rather then the norm and look to back him in next start expecting around 2-3 runs given up in six innings.

We can collate a range of statistical data to determine the exact reason why he has a 5.68 ERA and calculate the probability based on his profile, his health, and other statistical data to gauge whether the pitcher is likely to bounce back in his next start or whether he will continue to show regression in his overall numbers.

Or we can take the view that since pitchers rarely go nine innings, a 5.68 ERA could equate to a serviceable start where a pitcher goes seven innings and gives up 4-5 runs and will give a team every chance to win with an equal pitching matchup.

Let’s examine the ERA variables-

Night/Day

Certain pitchers have significant difference in their night/day splits. One such pitcher is Braden Looper who has excellent daytime statistics in comparison to his night splits. Why? There is no exact science to explain the discrepancies. We can rationalise it as maybe it is the way the ball comes out from the pitcher’s hand at a certain angle that makes it easier for pitchers to pick up his intent or his pitches tend to flatten out at certain temperatures, etc.

Home/Road

The more famous pitchers in recent times with schizophrenic home/road splits are Ervin Santana and Wandy Rodriguez. Whilst Ervin Santana enjoyed a breakout season, Wandy has yet to prove himself, but as I noted in my article, “Young Players to watch”, he is well worth watching as the problem can only be mental. So far he has given me a nice profit following him and I will continue to monitor his season closely. Both pitchers show very strong home splits but their road ERAs were horrible. There are some pitchers who also pitch better on the road due to the unfavourable dimensions of their home park thus showing improved performances on road games leading to the bookies undervaluing a particular pitcher.

Seasonal

On a year to year basis, there are statistical data charting exactly how a pitcher has done in a particular season. There may be a particular season where he had bad starts against a particular team leading to an inflated ERA against that particular team. An example of usage is Ricky Nolasco last season. He was the underdog against the Mets but I could not understand why given he had pitched so well leading the Marlins staff. His career ERA against the Mets was inflated but he is still a young pitcher. I looked closer at his first year stats and the answer was there. That first year skewed the numbers and was misleading, thus he was an undervalued pitcher as he was pitching for the unfashionable Marlins and his career record against the Mets looked ugly.

Team

As in sports, there are quirks. Who can explain why a pitcher like Tim Lincecum struggle against the Padres but can dominate the LA Dodgers who have the better hitting line-up? Why does Roy Oswalt exert dominance over the Reds over a long period of times no matter the evolving line-ups? Why does Doc Halladay struggle against the Rangers not only on the road, but at home? Why does Tim Wakefield dominate the Rays? In baseball, with large sample sizes, there are strong trends that will show when handicapping certain pitchers. Some books may choose to overvalue a favourite and thus seeking out answers in career ERA can lead to value plays.

Ballparks

There are certain ballparks that do not play to the strengths of pitchers. There are flyball pitchers, groundball pitchers and power pitchers and they all have their own favourable dimensions. Ballparks have their own bias, whether it is hitters/pitchers or left-handed/right-handed hitters and they also have differing dimensions. For the most part, most hitters do not show many significant spikes in their ERA and it will pay to check whether their team ERA can rationalise their pitching woes in any particular stadium.

Combining his current season ERA and placing it in context of the variables allows you to project how he will pitch by comparing his ERA. You can gauge his pitching line with his WHIP and BAA and make an informed forecast.

Next time we will discuss the WHIP and BAA (OBA).

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