Positional adjustments are a tricky subject to model. It’s obvious that an average shortstop should get more credit for defense than an average first baseman, but there are a wide variety of methods to calculate this credit. Some methods use purely offense to calculate the adjustments, while others have used players changing positions as proxy for how difficult each area is.

We’ll use a simplified version of the defense-based adjustments (which I’ll propose a change for later) for Part 1. This model looks at all players who have played two positions (weighted by the harmonic mean of innings played between the two). Then, it produces a number for how much better an average player performed at a certain position than another. After doing this for all 21 pairs of positions, we combine the comparisons into one scale, weighted by which changes happen the most often.

Example: the table below shows how all outfielders in 1961 performed when changing positions within the outfield (using Total Zone per 1300 innings):

• LF/CF: 14.5 runs/1300 better at LF, 4028 innings
• LF/RF: 10.4 runs/1300 better at LF, 9487 innings
• CF/RF: 7.4 runs/1300 better at RF, 6025 innings

After weighting each transition by the number of innings, we get an estimate that the LF adjustment should be -8.3, RF should be 1.0, and CF should be 7.3. (We’re assuming that players being better at a position means that that position is easier.)

I performed this calculation for all seven field positions (1B, 2B, SS, 3B, LF, CF, RF) for all years between 1961 and 2001. While using only seasons from the same year does away with any aging issues, the big problem with this analysis is that it doesn’t adjust for experience, as very few managers, ever, send full-time first basemen to play the outfield. This experience issue will be addressed in Part 2, but for now we just have to keep it in mind.

Finally, while I could have expanded this to 2015, the difference between UZR/DRS and TZ is so massive that using both would have created a lot of error in the graphs below.

The graphs (using loess regression to smooth the yearly data):

With yearly data:

With error bars:

Less smooth version:

Less smooth version with points:

A lot of positions have 4-run error bars, so it would be wise to take some jumps and drops with a grain of salt. However, it is interesting to note that corner outfielders (especially left fielders) appear to get much better at defense since the 1960s, while the right side of the infield has seemed to drop in quality. Also, for whatever reason, center field had a huge dip during the 1970’s.

During Part 2, I’ll analyze these graphs in depth, and propose adjustments to this simple model.

Steamer and ZiPS, the two main projection systems used at this site, have similar outlooks on the futures of most players. However, the two models vary widely in a few cases, and it can be confusing to figure out why.

To try to visualize exactly how ZiPS, Steamer and the FanGraphs Fan Projections looked at players, I first averaged all three systems’ 2016 predictions for each player. Then, after calculating how far each projection was from this average, I performed principal component analysis to compare the differences in outlooks for all 284 players. (Fan scores are adjusted so that they would have the same average as Steamer and ZiPS.)

I primarily looked at three predicted stats: wOBA (for general offense), Fielding (for general defense), and WAR per 600 plate appearances (for general value).

The results:

Projected Offense (wOBA):

(Each arrow points towards the direction where it projects a player higher; for instance on this graph, Daniel Murphy is much better liked by Steamer than by the Fans, while Colby Rasmus is much better liked by ZiPS than the Fans. Players towards the middle are well-balanced among the three.)

Projected Defense (Fld):

(This one is pretty crowded, but the players in the middle aren’t that interesting; it’s the ones on the outside we’re looking for.)

Projected Overall Value (WAR/600 PA):

It seems like ZiPS seems to favor lumbering home-run hitters more than the other two systems, but it’s tough to make any hard conclusions without a further analysis that eyeballing these graphs can’t provide.