Fixing the Chicago White Sox

Recently, I have noticed significant offensive production from the Chicago White Sox line-up.   According to the statistics from mlb.com, the White Sox rank 11th in the league terms of batting average and 7th in terms of runs scored (mlb.com).  Despite the good offensive production, the White Sox find themselves dwelling at the bottom of the AL Central with a 36-43 record.  The majority of the blame for the teams underwhelming record this year might be a result of poor pitching performances.  The White Sox rank 27th in the MLB, with a 4.38 team ERA, as of June 25th (mlb.com).  Even with the consideration of the faulty pitching, I do believe that this roster is capable of winning more frequently.  Thus, I dedicate this blog to discussing possible changes that could be made to the current Chicago White Sox line-up and pitching staff, which I believe could result in a better winning percentage.

As I mentioned in the preceding paragraph, the White Sox have an above average offense in terms of Runs and Batting Average.  Although, I do believe that with some tweaking of their current line-up, the White Sox offensive production could actually increase.  The main flaws within the current line-up originate from the middle of the line-up where Jose Abreu bats 4th, followed by Adam Dunn in the 5th spot.  Both Abreu and Dunn are free swinging hitters and have the propensity to strikeout.  To put this free-swinging concept into perspective, Abreu and Dunn have a combined total of 153 strikeouts.  The White Sox have a total of 661 offensive strikeouts, thus Abreu and Dunn contribute 23.2% of the teams total offensive strikeouts.  Roughly, one out of every four offensive strikeouts for the team come from either Abreu or Dunn.  Next, I sought to find the average of the two players respective batting averages.  Abreu and Dunn together have an average batting average of .251.  With all this being said, as a manager, I would never couple Abreu and Dunn consecutively in the line-up, especially in the middle part.  For instance, say two or possibly all of the first three hitters reach base with less than two outs.  The probability of the pitcher getting both Abreu and Dunn out is 56.1%, which is a percentage that favors the pitcher.  I now will present the Chicago White Sox line-up that I believe would be the most productive.

1)  Eaton. A
2)  Ramirez. A
3)  Abreu. J
4)  Gillaspie. C
5)  Dunn. A
6)  Beckham. G
7)  Flowers. T
8)  Viciedo. D
9)  DeAza. A

I now will provide my rationale for this specific line-up that I concocted.  When I look at the typical nine hitter line-up, I break it down into four different groups.  These groups are color coordinated above.

The first group of hitters, the green-highlighted names, I chose due to their ability to get on base and their patience at the plate.  These three hitters do not have the greatest batting averages, however their on-base percentages are decently high and are significantly greater than their batting averages.  For example, look at Adam Dunn, despite toting a .229 BA, he proudly owns a .359 OBP.  These three players also are the top three players on the White Sox, in terms of walks.

The next group of hitters, the teal-highlighted names, again were chosen with the consideration of their OBPs.  However, the main statistic that distinguishes them from the first group is their low number of strikeouts.  Ramirez and Beckham have 40 and 37 strikeouts respectively, on the season.  The low strikeout rates, in combination with their greater BAs show me that they are more of contact hitters and are more inclined to making productive outs, in a scenario when there are base runners.

Next, there are the orange-highlighted names.  This group of hitters are chosen based on their power and higher strike-out numbers.  I have these hitters positioned so that they are surrounded by contact hitters.  This is an attempt to hide their constant unproductive outs.  However, they are located in places auspicious to making significant offensive contributions.

The last group of hitters is uniquely composed by the statistic of On-Base Plus Slugging percentage.  I believe that the clean-up spot in the order should be someone with power, but not a player who just has power.  Through this I am insinuating that the clean-up hitter should have both good power and a high tendency to reach base.  Thus, I used the OPS statistic to determine who should be placed in these spots.  I understand that Conor Gillaspie has 0 HRs on the season, however he has a high OPS at .818.  This tells me that Gillaspie has good power to the gaps, which is crucial when playing in a large field such as U.S. Cellular Field.  I believe that he could truly thrive at this spot in the line-up and in turn help the White Sox offense.  He would provide a gap between the current dynamic, strike-out duo of Abreu and Dunn.

Now, I will transition to the suggestions I have for the White Sox pitching staff.  To begin, I will begin with the starting rotation.  Obviously, the White Sox have one of the better starters in Major League Baseball, this being Chris Sale.  However, the drop off in terms of talent after Sale is significant.  I believe that to solidify this rotation, the White Sox must make a move for a solid, number two starter.  A solid second starting pitcher could help bridge the gap between Sale and the back end of the rotation.  Quintana, Danks, and Noesi, I believe are good enough to handle these back end of the rotation duties.  Noesi being the only questionable one, however he has been performing better since he came to the White Sox.

The bullpen of the White Sox does need some refining, however for the most part I believe that what they have, if used properly could suffice.  In terms of a closer, I believe that Ronald Belisario is not the best option for them.  Belisario gives up way too many hits to properly fit this role, for his BAA is  .273.  I like my closers to not allow many base runners, thus eliminating the chances for the other team to score.  Personally, I would let Zach Putnam try to assume this role.  I say this because Putnam owns the lowest WHIP in the bullpen, with a 1.03.  He also has the lowest BAA on the team with a .192.  In terms of a set-up man, it is difficult to discern which player is the clear front runner for this position.  However, I am inclined to select Jake Petricka due to the fact that he has the lowest ERA of the bullpen pitchers, as well as a fairly low BAA.  The last main move that I would make if I was the General Manager of the White Sox would be to acquire a solid, left-handed arm for the bullpen.  As of this date, the White Sox have only Scott Downs who has an ERA of 6.08, with a WHIP of 1.65.

A Possible Relation between Offensive and Defensive Statistics

During my playing days, my father would always tell me that "Walks (defensively speaking) and Errors will kill you".  Of course, he said this in reference to the outcome of a game.  With consideration of his past advice and my forward statistical thinking, I decided to tamper with some current statistics in an attempt to correlate a players offensive and defensive abilities.  Keep in mind that none of my statistics are perfected, but are in beta (development) per say.  In my research, I constructed a statistic, which I refer to as Net Runs (NR).  Net Runs is based off of Bill James' statistic, Runs Created.  Runs Created is a statistic used to estimate the amount of runs a player or team created (http://sabr.org/sabermetrics/statistics).  The equation is as follows:

Runs Created= (((Total Bases) * (Hits + Walks))/(At-Bats + Walks))

Taking Runs Created into consideration, I sought to incorporate an additional concept factoring in the estimated amount of runs that a player costs his team on defense.  I figured that typically with a fielding error, the amount of bases the defense concedes is one.  Thus, four errors should equate to one run.  All of the proceeding aspects being taken in consideration, the equation for Net Runs is as follows:

Net Runs= Runs Created - (Errors/ 4)

Now, I am going to analyze this statistic with current players.  For my data set, I will use the top 50 offensive players in the MLB, as ranked by their Runs Created statistic, from this 2014 season.  The first column of the chart shows the players Runs Created rank.  The second column of the chart shows the players Net Runs rank.

RC Rank	NR Rank	Player	RC	Errors	Err./4	Net Runs
1	1	Troy Tulowitzki	70	2	0.5	69.5
2	2	Andrew McCutchen	63	4	1	62
3	3	Jose Bautista	61	2	0.5	60.5
4	4	Giancarlo Stanton	59	3	0.75	58.25
6	5	Yasiel Puig	56	0	0	56
5	6	Paul Goldschmidt	56	6	1.5	54.5
7	7	Mike Trout	55	2	0.5	54.5
8	8	Nelson Cruz	53	0	0	53
9	9	Carlos Gomez	52	2	0.5	51.5
10	10	Michael Brantley	50	1	0.25	49.75
13	11	Victor Martinez	49	3	0.75	48.25
11	12	Miguel Cabrera	49	4	1	48
12	13	Edwin Encarnacion	49	6	1.5	47.5
14	14	Jonathan Lucroy	48	2	0.5	47.5
15	15	Anthony Rizzo	48	4	1	47
17	16	Hunter Pence	47	3	0.75	46.25
18	17	Justin Upton	47	4	1	46
20	18	Melky Cabrera	46	0	0	46
19	19	Jose Altuve	46	2	0.5	45.5
16	20	Lonnie Chisenhall	47	9	2.25	44.75
23	21	Brandon Moss	45	2	0.5	44.5
22	22	Brian Dozier	45	4	1	44
24	23	Charlie Blackmon	44	2	0.5	43.5
26	24	Chase Utley	44	4	1	43
25	25	Todd Frazier	44	6	1.5	42.5
28	26	Freddie Freeman	43	3	0.75	42.25
30	27	Nick Markakis	42	0	0	42
31	28	David Ortiz	42	0	0	42
21	29	Josh Donaldson	45	13	3.25	41.75
29	30	Daniel Murphy	43	7	1.75	41.25
27	31	Matt Carpenter	43	8	2	41
32	32	Alex Gordon	41	0	0	41
33	33	Adam Jones	41	3	0.75	40.25
34	34	Jayson Werth	41	4	1	40
36	35	Jacoby Ellsbury	40	1	0.25	39.75
39	36	Mike Morse	40	1	0.25	39.75
35	37	Robinson Cano	40	3	0.75	39.25
37	38	Dexter Fowler	40	3	0.75	39.25
38	39	Justin Morneau	40	3	0.75	39.25
46	40	Neil Walker	39	1	0.25	38.75
47	41	Christian Yelich	39	2	0.5	38.5
40	42	Jose Abreu	39	3	0.75	38.25
42	43	Shin-Soo Choo	39	3	0.75	38.25
49	44	Seth Smith	38	0	0	38
41	45	Xander Bogaerts	39	6	1.5	37.5
43	46	Alexei Ramirez	39	6	1.5	37.5
45	47	Anthony Rendon	39	7	1.75	37.25
48	48	Adam LaRoche	38	3	0.75	37.25
50	49	Brett Gardner	37	0	0	37
44	50	Hanley Ramirez	39	9	2.25	36.75

I took the liberty of highlighting the players that have the largest gaps between their RC and NR ranks.  For the players with large positive changes (rank movement of at least 3 places), their names are highlighted in green.  Then for the players with large negative changes, their names are highlighted in red.  The greatest positive movement belongs to Christian Yelich, an outfielder for the Miami Marlins.  Yelich moved up a total of 6 spots.  Now, looking at the converse situation, Josh Donaldson, Third Baseman for the Oakland Athletics, owns the largest skid.  Donaldson descended a total of 8 spots.

I understand that the probability of committing errors varies with each position.  I also acknowledge that some of these players are designated hitters and have no risk of committing errors.  This statistic is just an attempt to try to combine the offensive and defensive statistics of a player.  I will work to try and refine this statistic for the future.  A factor which I wish to consider for Net Runs in the future is figuring out the exact amount of bases that a defensive players error actually yields.  I could even make the statistic more efficient through adjusting the equation so that the equation would produce the players net runs per game.

Net Runs Per Game= (Runs Created - (Errors/ 4)) / Games Played

*NOTE THAT ALL OF THE STATISTICS, WITH THE EXCLUSION OF NET RUNS, ARE NOT MINE.  THEY BELONG TO THEIR RIGHTFUL CREATORS.  ALL DATA I USED TO PERFORM MY CALCULATIONS IS COMPILED FROM BASEBALL-REFERENCE.COM.*