# A second look at Ian Kinsler

*Statistics current through Sept. 13.*

People all around the world of fantasy “hate” Ian Kinsler. It’s time for that to end.

In the preseason, I received a lot of criticism for ranking Ian Kinsler as my number two fantasy second baseman for 2011 behind Chase Utley, who was still healthy at the time. Some critics legitimately focused on the level of risk imposed by Kinsler’s health; I thought he was a top five or 10 second baseman even if inundated with injuries. For what it’s worth, Kinsler has appeared in 142 of the Rangers’ 148 games this season.

Other critics focused on his raw stats, claiming that Kinsler had never proven himself on par with the likes of Robinson Cano. In reality, as I pointed out, the two players rated very similarly over the past four seasons:

Name | GP | AB | AVG | HR | SB | R | RBI |
---|---|---|---|---|---|---|---|

Ian Kinsler | 498 | 2058 | 0.280 | 78 | 95 | 372 | 263 |

Robinson Cano | 640 | 2477 | 0.305 | 87 | 14 | 369 | 363 |

Kinsler prorated | 640 | 2645 | 0.280 | 100 | 122 | 478 | 338 |

Despite a clear differential in games played, they have comparable absolute home run and runs scored production, with Cano having a noticeable edge in batting average and RBI. Kinsler owned a huge advantage in stolen base totals. Cano had a substantial lead in games played over Kinsler.

Health is obviously a factor of value, but when you consider how close the two are in value even when one is constantly making trips to the disabled list, comparing their rate stats is not irrational. If we prorate Kinsler’s numbers to the same number of games played as Cano, Kinsler produced the better rate stats overall. Hence, my claim that Kinsler’s ceiling and floor were high enough, despite an ankle-injury-derailed 2010, to warrant top three consideration.

Eight months later, my widely panned ranking has turned out to be more or less correct. Kinsler has been the third best second baseman this year per Yahoo (second best, per my Z-Score sum calculations), behind Cano and Dustin Pedroia. Over the past 30 days, Kinsler has been the No. 1 fantasy second baseman. He has also been an elite player overall. So far through 2011, Kinsler’s season qualifies him as a top 30 overall fantasy player (No. 28, by Yahoo’s calculations) and top 10 fantasy hitter (via my Z-Score sums). Over the past 30 days he’s been even better, and Yahoo values him as a top 10 overall player (No. 7) over that period.

Cano (Yahoo’s No. 14 ranked overall, my No. 9 overall hitter in baseball via Z-Score sums), who I ranked as my No. 3 second baseman, has been the better fantasy baseball player. Thus, my argument of Kinsler over Cano was incorrect…right?

In terms of their results, yes. Objectively speaking, no matter how you slice their numbers on the season (which is honestly all that really matters), Cano has been the better fantasy player. Yahoo has their values pegged much farther apart than my Z-Score sums (below), but Cano’s produced more in the relevant fantasy categories:

Name | Team | G | AB | PA | AVG | HR | SB | R | RBI | ZAVG | ZHR | ZSB | ZR | ZRBI | ZSUM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Robinson Cano | Yankees | 146 | 577 | 624 | 0.305 | 26 | 8 | 96 | 111 | +1.04 | +2.14 | +0.20 | +2.49 | +3.04 | +8.91 |

Ian Kinsler | Rangers | 142 | 569 | 663 | 0.251 | 28 | 25 | 108 | 72 | -0.23 | +2.39 | +2.25 | +3.01 | +1.42 | +8.83 |

Z-Score sums are determined by taking the difference between a player’s value in a given stat from the league’s mean production in that stat, and then dividing by the standard deviation. This gives you a strong measure of relatively in evaluating how good players are in a given statistical category. Z-Scores, for example, are a useful tool for answering the question of which is more “valuable” in a vacuum—20 RBI or five stolen bases. By taking the Z-Score for each Roto category and summing them, we get a Z-Score sum which tells you what each player’s overall relative value is.

To get an accurate Z-Score sum, you need an accurate pool of players. The pool of useful players from which to calculate Z-Score sums will vary wildly from league to league, depending on the number of teams, the number of players per position per team, the format of play, etc. To simplify an election bias, I simply included all players with 100 or more plate appearances to determine the relative value of hitters for this exercise.

On one hand, this inflates the value of players who have more counting stats, as the players near the bottom of the plate appearance threshold —who probably have little fantasy value—drag down the “mean” and increase the standard deviation. At the same time, this effect occurs equally on all players in the pool. Hence, while the means and standard deviations may not be accurate representations of the true fantasy means and standard deviations of any given league, they have a relatively similar effect for rankings purposes. Alternatively explained, though the absolute Z-Scores *numbers* may be skewed, their *relativity* should be reliable for our purposes.

Mathematics aside, the point remains salient. Cano has been better, but their value has been pretty close and both have been pretty excellent.

But Kinsler should be doing a lot better—at least if you buy into BABIP-luck theories. So let’s look at BABIP luck-neutralized production of Cano and Kinsler and see who *should* be faring better.

To account for BABIP luck, I calculated the xBABIP of Kinsler and Cano using THT’s own xBABIP tool. Using each player’s xBABIP, I then calculated the number of hits each player should have expected to produce irrespective of luck. Using this expected hits total, I recalculated the player’s expected batting average.

Then, using the differential between each player’s expected hits (xH) total and his actual hits (aH) and holding each player’s production rates constant, I calculated an expected stolen base, runs scored and RBI differential to be added to/subtractedfrom each player’s actual 2011 numbers. I ignored the BABIP effect on home runs, as those are not balls in play and because I did not want to have to go through each player’s detailed game log to find “robbed home runs” and then also account for “lucky” home runs.

I also performed the same analysis and “number crunching” using each player’s career BABIP for comparison.

Here is each player’s relevant BABIP data:

Name | 2011 BABIP | 2011 xBABIP | Career BABIP |
---|---|---|---|

Ian Kinsler | 0.242 | 0.308 | 0.283 |

Robinson Cano | 0.320 | 0.324 | 0.322 |

Using some mathematical “reverse engineering,” here are how Cano and Kinsler’s BABIP-luck neutralized 2011 stats stack up using their career BABIPs:

Name | Team | AB | H | HR | R | RBI | SB | AVG | ZAVG | ZHR | ZSB | ZR | ZRBI | ZSUM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Robinson Cano | Yankees | 577 | 177.0 | 26 | 96.4 | 111.5 | 8 | 0.307 | 1.1 | 2.1 | 0.2 | 2.5 | 3.1 | 9.0 |

Ian Kinsler | Rangers | 569 | 162.7 | 28 | 117.2 | 78.1 | 27.1 | 0.286 | 0.6 | 2.4 | 2.5 | 3.4 | 1.7 | 10.5 |

And using their 2011 xBABIPs:

Name | Team | G | AB | xH | HR | R | RBI | SB | AVG | ZAVG | ZHR | ZSB | ZR | ZRBI | ZSUM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Robinson Cano | Yankees | 146 | 577 | 178.0 | 26 | 96.9 | 112 | 8.1 | 0.308 | 1.1 | 2.1 | 0.2 | 2.5 | 3.1 | 9.1 |

Ian Kinsler | Rangers | 142 | 569 | 174.6 | 28 | 122.8 | 81.9 | 28.4 | 0.307 | 1.1 | 2.4 | 2.7 | 3.7 | 1.8 | 11.6 |

As you might notice, when you strip out Kinsler’s poor BABIP luck, whether you use his career BABIP or 2011 xBABIP, he should have been a superior fantasy producer. In fact, Kinsler’s xBABIP adjusted stats would place him as the fifth overall player, behind Matt Kemp (13.5 Z-Score sum), Curtis Granderson (12.9), Jacoby Ellsbury (12.6) and Ryan Braun (11.6). That’s a hair ahead of Jose Bautista (10.2). Even if we hold Kinsler’s counting stats constant and adjust only his batting average to reflect his career BABIP, Kinsler’s aggregate production this season outweighs Cano’s. As it should be evident, Kinsler is an elite player of the highest order when healthy, even if he did not put it all together in 2012.

Of course, results are all that matter in fantasy, and in that regard, Cano has had the better 2011 season. However, for the future, it is clear that my initial hypothesis—that a healthy Ian Kinsler has top-tier player upside—rings true. For 2012, neither Kinsler or Cano will come cheap. However, Cano and Pedroia will likely cost more, meaning Kinsler, if healthy, could be a top 10 player at a relative discount

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About this “BABiP luck” thing – I’m not sure I totally buy it. If a player’s BABiP is consistently under .300, don’t you have to start thinking it’s not all attributable to luck, but something about the player’s hitting that’s to blame? Perhaps he’s a little bit lacking in the “hit ‘em where they ain’t” department? Maybe he’s a little too predictable in where his batted balls go, to the point where good scouting can reduce his average?

@Steve,

A player’s BABIP is unique, and not all are .300. Kinsler’s career rate is .283. hence, the adjustment for both career and xbabip rates. there is regression towards one’s own unique babip, but you are correct in that such regression is not always towards a mean of .300

@Eddie,

I did not know that, thanks for pointing it out!

Still not sure why kinslers list hits 240 every other year

In point leagues which value walks and hitting for extra bases, Kinsler has been the better player this season. I know that’s a small subset and all, but it’s something.

Ross,

Do you have data to support the claim? I cannot say I anecdotally agree.

Jeffrey, as a Rangers fan, I can tell you with reasonable certainty that Kinsler’s low BABIP is indeed a result of his uppercut swing, which leads to a large amount of popups. However, that hitting style works for him, and if he evened out his swing his home run totals would likely plummet.

I know I’m biased, but in my opinion Kinsler is one of the most underrated players in baseball. The problem from a fantasy perspective is that two of his most valuable assets, walks and defense, don’t show up in traditional fantasy formats.

@Sean/Ross,

I do not deny he has an uppercut swing that induces higher popup rates. His season and career rate (just under 11%) is a point or 2 higher than league average. However, even with that considered, his BABIP should be somewhere between .285 and .305

I honestly love Kinsler. He’s great

I love Kinsler. I’ve had him on every team for the past three seasons, even when I had to take him in I think it was the 2nd round? One year.

His BABIP is so low every year because he has a tendency to alter his swing path at time and swing for the fences, leading to pop ups. If he’d level out his swing he’d have more line drives and ground balls, both of which would lead to more hits and a higher BABIP.

Ian Kinsler

2011

home: .290 / .399 / .517, wRC+ 158

away: .203 / .289 / .413, wRC+ 90

and this isn’t at all a small sample thing.

career

home: .308 / .395 / .525, wRC+ 147

road: .241 / .313 / .409, wRC+ 92

Can anyone who watches him everyday explain what is going on with this?

@YP:

So you’re suggesting that a BABIP difference of .065 over 2800 evenly split plate appearances is the result of luck? I know BABIP is highly variable, but that’s more than two full seasons of data for each and a huge difference in BABIP.

Kinsler’s home/road splits are weird as he hits about the same amount of homers on the road and home…it all comes down to his luck on the road is just terrible but there is no discernible reason for it. I will say, after the first month or two, he has hit much better on the road. But his babip for everything from grounders, liners, flyballs, etc is terrible on the road despite his power numbers not really being inflated by Arlington. In 2008, he did hit over 300 on the road, however.

Okay, that makes some sense. But these wRC+ numbers are supposed to be park-adjusted. Is it assumed that Kinsler is playing all of his road games in neutral parks? I’d think they’d be adjusting his numbers based on the actual parks he’s played in.

If so, demonstrating this point with other Rangers and Rockies players just suggests to me that the accepted park factors are insufficiently accounting for how much those parks help(ed) hitters.

Yeah, pretty weird. I wonder how he’d do if he played on another team. I’d guess that his splits would be less pronounced