Advanced Stats: Pitching & Defense

Advanced Stats – Pitching/Defense

By: Colby Garrapy

Current as of: 7/28/10

While offense is one of the keys to success in baseball, there are still two factors that are typically not regarded as important: Pitching and defense. These are the prevention of offense for the opponent. If a team can prevent their opponents from scoring runs while they in turn are capable of scoring them, they have the best shot at winning.

This is where pitching and defense statistics can say a lot about a player. Just because someone is capable of winning 15 games doesn’t make them a great pitcher. What it means is that the pitcher prevented their opponents from scoring more runs than them in said game. Yes, this is what a pitcher should do, but it doesn’t account for those statistics a pitcher can control.

Out of all the statistics that relate to pitchers, there are four that a pitcher can control: home runs walks, hit by pitch, and strikeouts. While other stats are important to consider, if one wants to look at those that the pitcher can control, these are those stats.

Why only these select statistics?

The answer is because all other stats require the fielders behind the pitcher; all of which vary from team to team and have a vast difference in skill level. In order to generalize and judge a player solely on his ability, these stats must be weighed.

On a defensive scale there are many stats that can quantify a player’s ability to make the out. Organizations watch tape after tape of previous games in order to determine how an out was made and where it was made on the field. While this information isn’t accessible to myself, there are a few defensive stats that I can compute in order to give a better look at a player’s ability to turn outs rather than just comparing errors to others.

I want to start by analyzing those stats pitchers can control. This statistic is called Fielding Independent Pitching (FIP). This statistic analyzes a pitcher’s homeruns allowed, hit by pitches, walks allowed, and strikeouts. The formula looks something like this:

FIP= (HR*13 +BB+HBP-IBB) *3 – K *2 / IP

After we compute this formula an average league factor must be added to put it into an ERA format. Typically the league average FIP – league average ERA is added, but to make life simple, we will use the average league factor of 3.2 that is commonly used. Here are the Spinners top five pitchers in FIP:

FIP (min. 20 IP)

Keith Couch                              2.64

Hunter Cervenka                     3.65

Garrett Rau                               3.66

Tyler Wilson                            3.67

Ramon Mendez                       3.81

All five of these pitchers have a relatively good FIP. In other words, fielding aside, these pitchers have performed well. Their homerun, walks, and hit by pitch totals are relatively low and they strikeout a good amount of the batters they face. Cervenka is the one pitcher the posted a negative number before adding the league average factor.

Another statistic I will take a look at now is batting average on balls in play (BABIP). What this stat does is measure a player’s average on balls hit in play (excluding homeruns). It can be used for hitters and pitchers and is effective in weighing flawed seasons. It is difficult to maintain low or high BABIP, so typically if a player has a high BABIP one season it is realistic that a player will improve the following season. Vise versa for a player with a low BABIP, it is likely that player will regress the following season. An average BABIP is around .300, but can depend a lot on the team’s defense. Here is a look at the Spinners top pitchers in BABIP and the bottom pitchers in BABIP:


Hunter Cervenka            .278

Stephen Fox                     .286

Ramon Mendez               .295

Madison Younginer       .315

Garrett Rau                       .326

Bottom BABIP:

Tyler Wilson                   .370

Cesare Angeloni            .352

Charle Rosario               .333

Keith Couch                     .330

As you can see, only three pitchers fall below a .300 BABIP, while the rest are above. This can translate into a couple different things. One could be the three pitchers below .300 do a better job at preventing the ball from being batted, they are having a lucky season, or the defense plays better when they are pitching. For the pitchers above .300, it could mean the exact opposite. What we can take from this is how batters bat on balls in play against certain pitchers. Some of these like, like Wilson and Angeloni, should see a slight decrease while pitchers like, Cervenka and Fox, may see a rise in the future.

Now that I have covered a couple pitching statistics I want to take a look at one defensive stat that is clearer than your basic fielding percentage. All fielding percentage does is calculates the number of times a player cleanly handles a batted ball or thrown ball. Range factor calculates the number of outs a player participates in. How calculating range factor works is you divide a player’s putouts and assists by the number of innings or games he has played. Dividing by the number of games gives you his range factor per game. Dividing by innings and multiplying by 9 gives a player’s RF/9, which is what is commonly looked at.

(PO + A * 9)/IN = RF/9

Here is a look at the Spinners top players at each position:

Current as of 8/1/10

RF/9 (min. 75 innings)

C:              Chia-Chu Chen                          8.81

1B:             Miles Head                                 9.90

2B:             James Kang                               4.88

3B:             David Renfroe                           2.79

SS:             JT Garcia                                    5.43

LF:            Seth Schwindenhammer         1.86

CF:            Bryce Brentz                                2.67

RF:            Brandon Jacobs                        2.14

These are the players who participate in the most outs at their given positions with a minimum of 75 innings of work. One of the most surprising positions was centerfield where Brentz had a RF/9 of 2.67 while the speedy Felix Sanchez had 2.07. While .60 doesn’t seem huge, it is over half a play more a game Brentz participated in than Sanchez.

As we can see, stats say a lot about players. Even I was surprised by some of the range factors of the Spinners. As for the pitching statistics it was a clear indication as to who has performed well this season, and who has not. Mendez ranked as one of the top pitchers on the staff, but with his departure we will see a slight change in the way the Spinners play. These three stats I covered are only a select few of the hundreds that can be calculated.

This is an eye opener to what baseball stats can offer. It changes the way a player’s basic numbers are looked at and allows followers to examine another side of their performance. Winning is important, but if the team can’t perform well in hitting, pitching, and defense they will have a tough time doing so. That is where these types of stats can come into play and allow you to put aside typical stats that are reflected in the team’s performance and be able to examine a player solely on his performance. While these are only three different stats to quantify a player’s performance, it allows one to put typical defense and pitching statistics aside and exemplifies how each player can perform.


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