At least one other author has examined the influence of hand size on WR performance. Joe Redemann at numberFire explored relationships between hand size, several standard statistics, and “a player’s contribution to his team”, NEP, across multiple seasons. A mild relationship between hand size and catch rate (receptions / targets rate) was discernible (r = .17). Ultimately, Redemann demonstrated that the performance of elite WRs was relatively unrelated to hand size whereas a stronger—but still mild—relationship evidenced between the performance of ordinary WRs and hand size.
We should expect then to find that larger hands predict in fewer drops. We should also expect a minimal reduction in drops offered by larger hands. We shall extend the analysis by including TEs and RBs in addition to WRs.
NFL Combine data were procured from NFL Savant and passes dropped were extracted from Sporting Charts. Drop data were gathered for WRs, TEs, and RBs from the 2009-15 seasons. Combining Combine and drop data yielded 604 player-seasons from 238 players without checking for spelling inconsistencies between the two data sets. As regular readers know, I am lazy, so I pressed onward with this sample. Player height and arm length were included because of my intuition that doing so would better isolate the effect, if any, of hand size by isolating the effect of atypically large hands given medium or small stature.
| Totals | Medians | |||||||
|---|---|---|---|---|---|---|---|---|
| Seasons | ≥ Median Targets | Players | Handsize | Arm Length | Height | Drop % | Targets | |
| RB | 123 | 65 | 63 | 9.25 | 31 | 71 | 0.051 | 28 |
| TE | 103 | 52 | 42 | 9.875 | 33.125 | 77 | 0.046 | 35 |
| WR | 378 | 194 | 133 | 9.25 | 32.1 | 73 | 0.043 | 67 |
| TOTAL | 604 | 311 | 238 | |||||
| MEDIAN | 9.375 | 32 | 73 | 0.045 | 53 | |||
Table 1 displays basic information about the players within our sample. Medially, TEs presented with the largest hands and, for the included seasons, WRs dropped fewer passes while being targeted more frequently. Thus, position should be accounted for in the analysis. Also, we will need to account for season to season variability within players.
I constructed several models. For each model, the count of drops for each player-season was the dependent variable (actually, the rate of drops, or, drops / targets). Hand size, arm length, height, and position were predictor, independent, or explanatory variables—whichever jargon you prefer. Players were included as a random effect to account for each player’s variability in drops between seasons. Random effect also means that the models are constructed accounting for the differences between players that we are not measuring in this analysis; for instance, a factor as relevant but complex as coverages faced or as esoteric as blood type.
Three models were constructed. Excluded here, there is a table at the bottom of the page with pertinent model data.
- The first model included all 604 player seasons. In short, increased hand size predicted fewer drops; being a WR decreased drops.
- The second model included 311 player seasons from 127 players who accumulated ≥ median targets at their positions. Again, larger hands and being WRs predicted fewer drops. Interestingly, greater arm length predicted greater drops.
- In the third model, I included all 604 player seasons while accounting for whether ≥ median targets were accumulated in a given player-season (via dummy coding). Again, being a WR and having larger hands predicted fewer drops.
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| Figure 1. Expected Drops by hand size for NFL receivers. |
Why though would greater arm length predict more dropped passes in the group of receivers targeted at or above the median? It might be a spurious finding, but, perhaps players with longer arms drop errant passes at the fringes of their catch radii, passes beyond the range of players with shorter arms.
Alternatively, RBs are known to exhibit the highest drop rate. Likewise, consider that RBs are often catching passes coming out of the backfield where more defenders may be lurking; perhaps RBs are targeted with more less accurate passes due to their being a check down or hot receiver targeted when the QB is hurried; or by releasing from his blocking assignment into the flats, knowing a pass is coming, and having to reorient his body to catch. The process of reorienting with multiple proximal defenders might be more difficult for larger RBs, with longer arms. Unsurprisingly then, within each position, only for RBs is the raw correlation between drop rate and arm length appreciable—although this may be the result of small sample sizes:
- WR: r = .014, p = .805, n = 315
- TE: r = .133, p = .311, n = 60
- RB: r = .369, p = .004, n = 58
| Variable | Fixed-Effects | |||
|---|---|---|---|---|
| Estimate | SE | z | p | |
| Model 1: AIC = 2216.4 | Player Var & SD = .039, 0.197 | |||
| Intercept | -2.940 | 0.085 | -34.400 | 0.000 |
| Hand | -0.065 | 0.036 | -1.790 | 0.073 |
| Height | -0.002 | 0.056 | -0.030 | 0.978 |
| Arm | 0.067 | 0.051 | 1.330 | 0.185 |
| TE | -0.113 | 0.142 | -0.800 | 0.425 |
| WR | -0.172 | 0.093 | -1.840 | 0.066 |
| Model 2: AIC = 1298 | Player VAR & SD = .037, 0.193 | |||
| Intercept | -2.974 | 0.101 | -29.350 | 0.000 |
| Hand | -0.0840 | 0.0422 | -1.990 | 0.047 |
| Height | 0.112 | 0.064 | 1.760 | 0.079 |
| Arm | 0.028 | 0.067 | 0.420 | 0.674 |
| TE | -0.239 | 0.166 | -1.440 | 0.149 |
| WR | -0.199 | 0.111 | -1.790 | 0.074 |
| Model 3: AIC = 2207.2 | Player Var & SD = .039, 0.197 | |||
| Intercept | -2.786 | 0.094 | -29.570 | 0.000 |
| Hand | -0.058 | 0.035 | -1.670 | 0.095 |
| Height | 0.070 | 0.049 | 1.430 | 0.153 |
| Arm | 0.003 | 0.054 | 0.050 | 0.960 |
| ≥ Median Targets | -0.199 | 0.059 | -3.410 | 0.001 |
| TE | -0.131 | 0.137 | -0.960 | 0.340 |
| WR | -0.189 | 0.091 | -2.090 | 0.036 |
glmer function within the lme4 package was employed in the GLMM. A Poisson distribution was specified; a negative binomial distribution yielded essentially identical results but the models did not converge. Data and R code available upon request, of course.