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Cal in the NFL Draft, Evans Hall Football edition: What is a draft pick worth?

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Soon we will see one of the best moments in the NFL season: The NFL Draft. It is a time where auction theory, valuation, scouting, fit, chance, and faith all intersect. In this article I will try to take in historical data on draft the draft and use statistical analysis to determine the value of each draft pick. By knowing the value of each draft pick each team can make better decisions when trading draft picks.

Chris Trotman/Getty Images

There are many formulas for calculating the draft pick value. The most famous chart was developed by Jimmy Johnson when he was the HC for the Dallas Cowboys. Another attempt at calculating the value of each draft pick was conducted by the "The Harvard Sports Analysis Collective". The HSAC used standardized Career Approximate Value overtime and conducted a simple regression on prick value to calculate the pick value.

In this article I will conduct a more indepth analysis of 8,411 draft picks from 1970 to 2015 from pick #1 to pick #224. All of the players were chosen from pro-football-reference's database of players picks and values. In my case I decided to use R and STATA. The R code was used to clean-up the data set from the initial saved output from the website and then the data was processed by STATA for the process of statistical analysis.

tl;dr My chart emphasizes the value of the midlevel picks more than the JJ or HSAC. This falls in-line with my idea that it is important to build the core of the team through the draft, surely you can find blue-chip talent in the draft. However, when we evaluate the picks in the draft, the value of a few solid contributors from the middle of the draft can make a huge difference in the fortunes of a team. It may not be a 1:1 difference, but I would argue that outside of a handful of truly great players, few are worth more than 3-4 good players.

Methodology

Dependent Variables

I will first start with the process that I was thinking about: what makes a successful draft pick? Of course we have advanced data such as AV for per player long-term performance. However, I think that there are more dimensions to the analysis that can point towards a successful career. So from the available data from PFR, besides the Career AV (CarAV), I picked the following:

  • Pro Bowl Selection (PB)
  • All-Pro Selection (AP)
  • Years Played (YP)
  • Games Started (GS)

The first two are imperfect data points when measuring the performance of the player. We often have players getting those accolades without merit (ex. Jeff Saturday in his last year getting the Pro Bowl nod) or being snubbed completely (ex. Lavonte David last year in the All Pro, or Delanie Walker). However, in general it should give us a glimpse at performance levels.

Years played points more towards the longevity of a player, especially in the mid to late rounds, can mean a lot for a team to have a player that lasts a long time even if they are not exceptional players. Likewise with game started, having a player that is a starter but not PB or AP level, can give a team mid-level stability for a team.

However, I wanted to normalize the relationship between all variables to see the relationship better. Value of 100 for each of the variables indicates the average for the variable. Here are the averages for each dependent variable for reference:

Average Values for Reference
Variable Avg.
Career AV 20.42
Pro Bowls 0.4
All Pro 0.1
Years Played 2.68
Games Started 40.39

Independent Variables

Thus our dependent variables are such : CarAV, PB, AP, UP, and GS.

Additionally, we will have these following independent variable : Log of pick (which then will be translated into a relationship between the value of a pick and a pick).

The reason I added the is to adjust for the curvature of the data that is constrained by the pick value as well as the minimum value of each dependent variable. See the following charts to see why:

CarAVPick Piotr

GSPick Piotr

Finally, I will use the QB as the benchmark for the data controlled by the different positions. This means that the coefficients from the regression on the positions will indicate the adjustment we will need to take on a per position level.

Model Outcomes

Simple Model with Pick Value

First I started with a simple model that tries to predict the value of the pick for each of the dependent variable using only the natural log of the pick.

Model1 Piotr

What we can see that the Log of each pick is statistically significant and negative. This is a linear-log model where the relationship between the dependent and independent variable is... complicated and not relevant to the conclusion. Which is the following table:

Pick Career AV Pro Bowl All Pro Years Played Games Started
1 322.6229248 585.3272114 672.2598686 333.3015442 327.52948
2 286.678009 510.3559589 586.1539116 295.6324463 290.7923584
3 265.6516113 466.5005817 535.7851067 273.5974426 269.3025208
4 250.7331085 435.3847065 500.047924 257.9633484 254.0552368
5 239.161438 411.2493305 472.3279839 245.8366241 242.2285156
6 229.7066956 391.5293293 449.6791496 235.92836 232.5653992
7 221.7128143 374.8562946 430.5298271 227.5510406 224.39534
8 214.788208 360.4134235 413.9419365 220.294281 217.3181152
9 208.6802673 347.6739521 399.3103752 213.8933563 211.0755463
10 203.2165375 336.2780781 386.2219963 208.1675415 205.491394
11 198.2739716 325.9692707 374.382122 202.9878998 200.4399109
12 193.7617798 316.5580463 363.5731621 198.2592773 195.8282776
13 189.6109772 307.9006062 353.6299248 193.9093628 191.585968
14 185.7679138 299.8850117 344.4238396 189.881958 187.6582031
15 182.1901093 292.4227009 335.8532524 186.1325378 184.0015564
16 178.8433075 285.4421711 327.8359795 182.6251984 180.5809937
17 175.6994629 278.8849812 320.3049126 179.3305511 177.3678589
18 172.7353668 272.7026844 313.2044182 176.2242737 174.3384247
19 169.9315643 266.8547382 306.4879265 173.2859955 171.4728394
20 167.2716217 261.3068104 300.1160393 170.4984589 168.7542725
21 164.7414856 256.0296345 294.0550804 167.8469543 166.1683655
22 162.329071 250.998003 288.2761497 165.3188171 163.7027893
23 160.0239105 246.1900806 282.7541618 162.9030914 161.3468323
24 157.8168793 241.5867939 277.467205 160.5901947 159.0911407
25 155.6999512 237.1714649 272.3960991 158.3717194 156.9275665
26 153.6660614 232.9293385 267.5239372 156.2402802 154.8488464
27 151.7089386 228.8473072 262.8356438 154.18927 152.848587
28 149.822998 224.9137592 258.3178825 152.2128754 150.9210815
29 148.0032654 221.1182728 253.9586906 150.3058472 149.0612335
30 146.2451935 217.4514332 249.7472649 148.4634552 147.2644348
31 144.5447998 213.9048786 245.6739769 146.6815033 145.5265656
32 142.8983917 210.4709187 241.7299919 144.9561157 143.8438568
33 141.3026428 207.1426258 237.9073906 143.2838135 142.2129517
34 139.7545471 203.9137135 234.198925 141.6614685 140.6307373
35 138.2513275 200.7783985 230.5979424 140.0861359 139.0943756
36 136.790451 197.7314167 227.0984306 138.555191 137.6013031
37 135.3696136 194.7679462 223.6948204 137.0661926 136.1491547
38 133.9866638 191.8834858 220.3819695 135.6169128 134.7357178
39 132.6396332 189.0739613 217.1551781 134.2052765 133.3590088
40 131.3267212 186.3355579 214.010067 132.8293762 132.0171509
41 130.0462189 183.6647968 210.9426384 131.4874573 130.7084351
42 128.7965698 181.058382 207.9491234 130.1778717 129.4312439
43 127.5763397 178.5133076 205.0260429 128.8991089 128.1841125
44 126.3841629 176.0267353 202.1701927 127.6497421 126.9656601
45 125.2187729 173.596056 199.3785057 126.4284515 125.7745895
46 124.07901 171.2188129 196.6481895 125.2340088 124.6097031
47 122.9637527 168.8927021 193.9766045 124.0652618 123.4698715
48 121.8719711 166.6155415 191.3612328 122.9211044 122.3540192
49 120.8027115 164.3853626 188.7998314 121.80056 121.2612
50 119.755043 162.2001972 186.2901268 120.7026367 120.1904373
51 118.7281265 160.0583363 183.8301659 119.6264648 119.140892
52 117.7211609 157.9580708 181.4179649 118.5711899 118.1117249
53 116.7333603 155.8978138 179.0517082 117.5360184 117.1021652
54 115.7640305 153.8760395 176.7296715 116.5201874 116.1114655
55 114.8124847 151.8913898 174.4502525 115.5230026 115.1389465
56 113.8781052 149.942522 172.2119408 114.5438004 114.1839752
57 112.9602432 148.0281086 170.013195 113.5819092 113.2458801
58 112.0583496 146.1470127 167.8527184 112.6367569 112.3241119
59 111.1718674 144.2980442 165.7291527 111.7077484 111.4180908
60 110.300293 142.4801807 163.6413002 110.7943726 110.5273056
61 109.4431381 140.6923847 161.5879784 109.8960953 109.6512604
62 108.5998993 138.9336262 159.5680046 109.012413 108.7894363
63 107.7701645 137.2030277 157.5803795 108.1428757 107.9414139
64 106.9534836 135.4996586 155.6240273 107.2870255 107.1067429
65 106.1494675 133.8226948 153.6980019 106.4444427 106.2850037
66 105.3577423 132.1713657 151.8014183 105.6147385 105.4758224
67 104.5779037 130.5448399 149.9333305 104.797493 104.6788025
68 103.8096466 128.942461 148.0929604 103.9923859 103.8936081
69 103.0525818 127.3634357 146.2794228 103.1990051 103.1198654
70 102.306427 125.8071613 144.4920082 102.4170609 102.3572617
71 101.5708313 124.2729053 142.7298851 101.6461792 101.6054535
72 100.8455429 122.7601643 140.992466 100.8861084 100.8641815
73 100.130249 121.2682514 139.2789803 100.1364975 100.1331177
74 99.42469788 119.7966633 137.5888252 99.39710236 99.41201782
75 98.72861481 118.34482 135.9213676 98.66763306 98.70059204
76 98.04175568 116.9122257 134.2759972 97.9478302 97.99859619
77 97.36387634 115.4983463 132.6521263 97.23742676 97.3057785
78 96.69471741 114.1026707 131.0491753 96.53617859 96.62187195
79 96.03412628 112.7248478 129.4667091 95.84389496 95.94671631
80 95.38180542 111.3642979 127.9040947 95.16028595 95.2800293
81 94.73760223 110.0206623 126.3609047 94.48518372 94.62162781
82 94.10131836 108.6935368 124.8366737 93.81837463 93.97131348
83 93.47274017 107.3825016 123.3309212 93.15964508 93.32888031
84 92.85168457 106.0871449 121.843174 92.50879669 92.69413757
85 92.23796844 104.8071003 120.3730202 91.86564636 92.06689453
86 91.63144684 103.5420704 118.9201088 91.23003387 91.44700623
87 91.03192902 102.2916355 117.4839592 90.60175323 90.83427429
88 90.43925476 101.0554829 116.0642128 89.98065186 90.22853851
89 89.85328674 99.83330727 114.6605263 89.36657715 89.62965393
90 89.27387238 98.62480354 113.2725334 88.7593689 89.03746796
91 88.70085907 97.42965889 111.8998833 88.15886688 88.451828
92 88.13410187 96.24755287 110.5422173 87.56492615 87.87258148
93 87.5734787 95.07824898 109.1992455 86.97740936 87.29959869
94 87.01883698 93.92141914 107.8706017 86.39616394 86.73273468
95 86.47007751 92.77686501 106.5560646 85.8210907 86.17188263
96 85.92706299 91.64428139 105.2552605 85.25202179 85.61689758
97 85.38967896 90.52344704 103.967968 84.68886566 85.06767273
98 84.85778809 89.41407204 102.6938248 84.1314621 84.52406311
99 84.33131409 88.31598854 101.4326553 83.5797348 83.9859848
100 83.81013489 87.22894478 100.1841583 83.03355408 83.45331573
101 83.29414368 86.15272713 98.94810486 82.49280548 82.9259491
102 82.78321838 85.08708382 97.72419357 81.9573822 82.40376282
103 82.27729034 84.03185844 96.51224518 81.42718506 81.88668823
104 81.77623749 82.98679161 95.31196594 80.90209198 81.37458801
105 81.28000641 81.95178413 94.12324142 80.38205719 80.86742401
106 80.78844452 80.92652321 92.94570923 79.86692047 80.36502838
107 80.30152893 79.91095924 91.77931595 79.35665131 79.86738586
108 79.81912231 78.90478325 90.62369919 78.85110474 79.37434387
109 79.34116364 77.90788841 89.47874832 78.35021973 78.885849
110 78.8675766 76.92012596 88.34428024 77.85391998 78.40182495
111 78.39827728 75.94128227 87.22006226 77.36209869 77.92218018
112 77.93319702 74.97125816 86.10597229 76.87471771 77.44685364
113 77.472229 74.00979805 85.0017128 76.39163208 76.97571564
114 77.01533508 73.05684471 83.90723038 75.91282654 76.50875854
115 76.56242371 72.11219978 82.82228088 75.43818665 76.04586792
116 76.11344147 71.17575264 81.74675369 74.96767426 75.58699036
117 75.66829681 70.24729729 80.68040466 74.50117493 75.1320343
118 75.22695923 69.32678795 79.62318039 74.03866577 74.68096924
119 74.78935242 68.41406441 78.57490158 73.5800705 74.23371887
120 74.35538483 67.50892067 77.53532791 73.12528229 73.79018402
121 73.92502594 66.61131096 76.50440216 72.67428589 73.3503418
122 73.49822998 65.72112465 75.48200989 72.22701263 72.91413879
123 73.07489014 64.83815956 74.46790695 71.78337097 72.4814682
124 72.65499115 63.9623661 73.46203613 71.34333038 72.05231476
125 72.23845673 63.09358406 72.46422577 70.90681458 71.62660217
126 71.82525635 62.23176384 71.47441101 70.47379303 71.2042923
127 71.41529846 61.37670326 70.49235344 70.04417419 70.78530121
128 71.00857544 60.5283947 69.51805687 69.61794281 70.36961365
129 70.60502625 59.68669319 68.55134392 69.19503021 69.95716858
130 70.20455933 58.85143471 67.59203339 68.77536011 69.54787445
131 69.80718994 58.02262306 66.64012718 68.35892487 69.14174652
132 69.41283417 57.20010567 65.69544983 67.94565582 68.73870087
133 69.02146912 56.38382912 64.75793839 67.53551483 68.33870697
134 68.63299561 55.5735836 63.82735825 67.12841034 67.94168091
135 68.24744415 54.76942635 62.90376663 66.72436523 67.54763031
136 67.86473846 53.97120094 61.98698807 66.32329559 67.15648651
137 67.4848175 53.17880058 61.07690239 65.92516327 66.76819611
138 67.10767365 52.39217949 60.17345047 65.52992249 66.38273621
139 66.73325348 51.61122894 59.27651596 65.1375351 66.00006104
140 66.36151886 50.83590317 58.38603592 64.74797821 65.62014008
141 65.99240875 50.06604195 57.50183678 64.36116028 65.24289703
142 65.62592316 49.30164909 56.62391663 63.97709656 64.86833191
143 65.26200867 48.54261971 55.7521553 63.59572601 64.4963913
144 64.90063477 47.78890228 54.88649559 63.21702194 64.12705994
145 64.54176331 47.04039383 54.02681923 62.84093475 63.76027679
146 64.18534088 46.29699135 53.17300701 62.46741486 63.39599609
147 63.83136749 45.55869484 52.32505894 62.09646225 63.03422165
148 63.47978973 44.82540131 51.48285675 61.72801971 62.67489624
149 63.13058853 44.09705925 50.64634132 61.36206436 62.31799698
150 62.78370667 43.37356377 49.81539345 60.9985466 61.96347046
151 62.43915558 42.65491676 48.99001408 60.63746643 61.61132431
152 62.09685135 41.94096375 48.17002487 60.27874374 61.26147461
153 61.75679398 41.23170471 47.35542583 59.92237854 60.91392517
154 61.4189682 40.52708626 46.54615831 59.56834412 60.56865311
155 61.08331299 39.82700729 45.7421031 59.21659088 60.2256012
156 60.74981308 39.13141441 44.94320154 58.86709213 59.88475037
157 60.41846466 38.44030952 44.14945364 58.51984787 59.5460968
158 60.08921432 37.75358772 43.36074066 58.1748085 59.20959091
159 59.76202011 37.07114792 42.57694387 57.83191681 58.87518692
160 59.43690109 36.39303875 41.79812288 57.49120331 58.54290009
161 59.11381149 35.71916008 41.02415943 57.15261459 58.21268845
162 58.79269791 35.04940701 40.25493455 56.81610107 57.8844986
163 58.47358704 34.38383102 39.49050772 56.48168564 57.55835724
164 58.1564064 33.72227859 38.73070127 56.14928818 57.23418427
165 57.84115601 33.06474781 37.97551513 55.81891632 56.9119873
166 57.52783203 32.41124058 37.22494924 55.49056244 56.59175491
167 57.216362 31.7616024 36.47882605 55.16415405 56.27342224
168 56.90677261 31.11588287 35.73720479 54.83971405 55.95700836
169 56.59901428 30.47397995 34.99996686 54.51718903 55.6424675
170 56.2930603 29.83584213 34.26705313 54.19655991 55.32976913
171 55.98891068 29.20146942 33.53846312 53.87782288 55.01891708
172 55.6865387 28.57080984 32.81413841 53.56094742 54.70988464
173 55.3859024 27.94376087 32.09396029 53.24589157 54.40261841
174 55.08702087 27.32037354 31.37798738 52.93267059 54.0971489
175 54.78984451 26.70054483 30.66610241 52.62123871 53.7934227
176 54.49435043 26.08422327 29.95824432 52.31156921 53.49141693
177 54.20053482 25.47140884 29.25441551 52.00366592 53.19112778
178 53.9083786 24.86205006 28.55455494 51.69749451 52.89253235
179 53.61785507 24.25609517 27.85860348 51.39303207 52.59560394
180 53.32896042 23.65354419 27.16656208 51.09028244 52.30034256
181 53.04167557 23.05434561 26.47836971 50.7892189 52.00672913
182 52.75594711 22.4583962 25.79391098 50.48978424 51.71470261
183 52.47180557 21.86574757 25.11324215 50.19200897 51.42429352
184 52.18919373 21.27629665 24.43624592 49.89584351 51.13545609
185 51.90811157 20.69004333 23.76292229 49.60128021 50.84818268
186 51.62856674 20.10698771 23.09327126 49.30832672 50.5624733
187 51.35050583 19.52702653 22.42717552 49.01692581 50.27828598
188 51.07392883 18.95016003 21.76463127 48.7270813 49.99560928
189 50.79883194 18.37638783 21.10564423 48.43879318 49.71445084
190 50.52517319 17.80560708 20.45009041 48.15200424 49.43476105
191 50.25294495 17.23781753 19.79797363 47.86672211 49.1565361
192 49.98215485 16.67301941 19.14929199 47.58293915 48.879776
193 49.71277237 16.11116123 18.50398636 47.30063629 48.60445404
194 49.44477463 15.55219126 17.8619976 47.01978302 48.33055115
195 49.17813492 14.99605751 17.2232666 46.74035645 48.0580368
196 48.91288376 14.44281244 16.58785439 46.46237946 47.78693771
197 48.64899063 13.8924036 15.95569992 46.18582916 47.51722717
198 48.38640976 13.34472847 15.32668304 45.91064835 47.24885941
199 48.12516022 12.79983807 14.7008667 45.63687134 46.98185349
200 47.86522293 12.25768185 14.07818794 45.36446762 46.71618652
201 47.6065979 11.71825886 13.45865059 45.09343338 46.45186234
202 47.34923553 11.1814661 12.84213257 44.82372665 46.1888237
203 47.09313202 10.64730358 12.2286377 44.555336 45.92707825
204 46.83831406 10.11582279 11.61822128 44.28829575 45.66664124
205 46.58473206 9.586920738 11.01076698 44.02255249 45.4074707
206 46.33238602 9.06059742 10.4062748 43.75810242 45.14956665
207 46.08125305 8.536801338 9.804683685 43.49492264 44.89289856
208 45.83132935 8.015532494 9.205997467 43.23301315 44.63746643
209 45.58261871 7.496790886 8.610212326 42.97237015 44.38327408
210 45.33509827 6.980525017 8.017271042 42.71297455 44.1302948
211 45.08873367 6.466683388 7.427112579 42.45479584 43.87850571
212 44.84353638 5.955265045 6.839740753 42.19783783 43.62790298
213 44.59949875 5.446271896 6.255149841 41.9420929 43.37848663
214 44.3566246 4.93970108 5.673343658 41.68756866 43.13026047
215 44.11486435 4.435453415 5.094203949 41.43421173 42.88317108
216 43.87421417 3.933525085 4.517730713 41.18201828 42.63721848
217 43.63470459 3.433969498 3.943981171 40.93101883 42.39242554
218 43.39625549 2.936630249 3.372776031 40.68113327 42.1487236
219 43.15892029 2.441614151 2.80424118 40.43241119 41.90615845
220 42.92267227 1.948865891 2.238311768 40.18483353 41.66470337
221 42.68748856 1.458335876 1.674926758 39.93836594 41.42433548
222 42.45336533 0.970024109 1.114089966 39.69301605 41.18505478
223 42.22031021 0.48392868 0.555801392 39.44878006 40.94686127
224 41.98828888 0 0 39.20563126 40.70972824

We can see here, from this very long table, that teach of the indicators show a fluctuating set of values.

The value of the 1st overall pick:

  • in my table is two 22nd (15th if  using the Pro Bowl measure) overall picks or three 63rd (37th if using the Pro Bowl measure) overall picks, or four 107th overall picks
  • in JJ's table is two 7th overall, or three 16th overall, or four 25th overall,
  • and in HSAC's table two 20th overall, or three 39th overall, or four 69th overall.
We can see that I am valuing the 1st overall pick the less relative to the alternatives, especially when it comes to counting the later round picks where it takes three 63rds vis-a-vis 16th or 39ths. I think it has to do with the high amounts of variance for each 1st overall pick dragging the value more while emphasizing the importance of the mid-upper picks that build a team's core. Especially in the 3rd and 4th round players (65th to 128th overall).

Conclusions From the Model

As we can see here, there are certain deviations from traditions in this model vis-a-vis accepted wisdom. The way I see the draft is unique in football. In no other sport does a 6th round QB get 2 League MVPs and 3 Championship MVPs. But more often than not, most teams are dependent on the performance of their mid-round picks to fill in the gaps between the blue chips.

Of course there is a lot of variance in the draft process. With the #1 pick we can get Peyton Manning or JaMarcus Russell, with the 195th pick Antonio Brown or a player that washes out of the league within a year, and with a UDFA, someone who isn't even a factor in my analysis we can get a grocery bag boy named Kurt Warner.

Class Dismissed