Estimating MLB Player Run Productivity

Yesterday, I went though a step-by-step guide as to how to determine the marginal product of various MLB team statistics with respect to run productivity at the team level. Yesterday, I treated each team as equal - which may not exactly be true since teams play in different ballparks and this may affect run production. I will come back to this later this month and re-do the analysis by breaking out each team in the overall analysis. I might also just look at different leagues as the AL might be more run producing since pitchers do not bat. It should not take long, so hopefully I will have this done soon.

Today, I want to use the estimates at the team level (see the end of the blog for the actual numbers), and use them to estimate the runs scored productivity at the batter level for last (2011) season. This is not all that difficult, so here are the steps.

Step 1: Open the Batting.csv file and copy the batting data to another file. (You do not have to do this, but I do, so I am calling this step 1).

Step 2: Copy the regression results (estimated coefficients) from the MLB team regression to the right of the player data.

Step 3: Create a formula for the first players run productivity. This is done my taking the coefficient of a single times the number of singles for the first player, plus the coefficient of a double times the number of doubles for the first player, plus the coefficient of a triple times the number of triples for the first player. Keep doing this for all ten variables. Once you have this formula, then copy the formula for the first player and paste it for all the players that year. Note: you might want to put a $ sign in front of the row number of the coefficient in the formula so that Excel keeps referring back to that cell. For example if the coefficient of a single is in cell AE3, then type in $AE$3 instead. This will force Excel to always use that cell when calculating the coefficient of a single. Do this for each of the ten variables.

Step 4: Sort the data by whatever you label the cell for the formula above. I called it Runs Scored, and sort by largest to smallest. Doing so yields the following. Note: that the batters name is using Mr. Lahmen's labeling, but you should be able to figure out who is who rather easily. Here are the top 50, with Miguel Cabrera the most productive batter last year, just above Jose Bautista.

Rank	playerID	teamID	lgID	Runs Scored
1	cabremi01	DET	AL	114.74
2	bautijo02	TOR	AL	113.97
3	ellsbja01	BOS	AL	111.70
4	kempma01	LAN	NL	110.94
5	braunry02	MIL	NL	108.43
6	vottojo01	CIN	NL	106.54
7	gonzaad01	BOS	AL	105.99
8	fieldpr01	MIL	NL	103.42
9	grandcu01	NYA	AL	102.57
10	pedrodu01	BOS	AL	97.77
11	uptonju01	ARI	NL	93.97
12	gordoal01	KCA	AL	92.97
13	konerpa01	CHA	AL	91.48
14	youngmi02	TEX	AL	90.95
15	canoro01	NYA	AL	90.92
16	pujolal01	SLN	NL	88.55
17	berkmla01	SLN	NL	88.48
18	kinslia01	TEX	AL	86.93
19	ortizda01	BOS	AL	86.04
20	teixema01	NYA	AL	85.38
21	ramirar01	CHN	NL	85.12
22	tulowtr01	COL	NL	84.38
23	reyesjo01	NYN	NL	82.24
24	mccutan01	PIT	NL	82.19
25	morsemi01	WAS	NL	81.96
26	stantmi03	FLO	NL	81.73
27	cabreme01	KCA	AL	80.32
28	zobribe01	TBA	AL	79.82
29	phillbr01	CIN	NL	78.12
30	napolmi01	TEX	AL	77.13
31	avilaal01	DET	AL	76.96
32	butlebi03	KCA	AL	75.95
33	francje02	KCA	AL	75.69
34	gonzaca01	COL	NL	75.55
35	swishni01	NYA	AL	75.55
36	martivi01	DET	AL	75.44
37	castrst01	CHN	NL	75.15
38	beltrad01	TEX	AL	74.48
39	howarry01	PHI	NL	74.10
40	victosh01	PHI	NL	73.99
41	hamiljo03	TEX	AL	73.48
42	cabreas01	CLE	AL	73.33
43	markani01	BAL	AL	73.21
44	santaca01	CLE	AL	73.07
45	bruceja01	CIN	NL	71.89
46	jonesad01	BAL	AL	71.66
47	peraljh01	DET	AL	71.14
48	penaca01	CHN	NL	71.04
49	longoev01	TBA	AL	70.86
50	hollima01	SLN	NL	70.37