# Cracking the Code: Creating Auction Values

I have used this method successfully for the past decade in my home league and wanted to share in case it provides value to others. Examples for my league are in parenthesis:

Using previous year’s data, take the first ADP for each position that goes for \$1 in the draft. (QB21, RB52, WR55, TE18).

Using previous year’s data, list the points per game for each position by ADP and the percentage that each ppg is relative to the sum of the overall position.

Calculate the VORP for each player by subtracting the respective ppg from the ADP from the first player in that position that goes for \$1. (QB1 = 30.3 - 16.1 = 14.2 VORP)

Add up the VORPs for each of the positions (QB=102, RB=288, WR=287, TE=51).

Calculate the VORP percentage by position (QB=102/729=14%)

Multiply total auction dollars by this percentage for each position.

For each position, multiply this number by number of players you plan to roster for each starting position.

Sum up the four numbers for QB, RB, WR, TE

Take this number and divide by your total auction dollars.

Take the total VORP for each position and divide by number from step 9. This is the number of auction dollars that should be allocated to each position by the league.

Finally, take the percentage of each player from step 2 and multiply the respective position value from step 10. This is the value that each player should be assigned.

I like this method, because it leverages historical data, with ADP and quantity of starting positions. Happy to share more details if there is interest or answer any questions.

Sorry, but this is nonsense.

In your own example, QBs are worth 14% of the auction budget, so by your system, everyone should spend \$28 on a QB in a standard \$200 budget auction draft.

If anyone spends \$28 on a QB not named Mahomes, they deserve to lose.

Nope, the example shows that VORP is 14%. I have RB and WR at 40% each and TE at 7% for my specific league. Translating this information from the next step to auction values for my league based on \$2,800 (14 team league) yields total QB spend at \$146 or \$10 per team.

You’re going to have to walk me through that.

14% of \$2,800 is \$392, or \$28 per team, as I stated initially.

You need to move to the next step (Multiply total auction dollars by this percentage for each position). Example is below:

Total VORP for Q1-Q21 is 102.7. This is 14% of total VORP (QB, RB, WR, TE). Now multiply total auction dollars (I use \$2677 since we use funds for IDP also) by 14% (\$2677x0.14=\$377). This value is now multiplied by one since my league only starts one QB. For RB and WR this is multiplied by 3 and TE is 1 since these are starting rosters for my league. Totaling after multiplying by these values give a sum of \$6901. This value now needs to be normalized back to 2800 and that is done by dividing \$6901/\$2800 = \$2.58. This value for QBs is \$377/\$2.58 = \$146.

Yeah, I dunno, I’m a lifetime A student in math, and this still seems rather arbitrary.

What if this was a normal league where there aren’t 21 QBs who go for more than \$1?

Then your total QB VORP is more like 50, and you shouldn’t spend more than \$5 on a QB.

So it’s sort of like your metric is dependent to some extent on how savvy the rest of your league is.

I guess if it works for you, great, but I don’t see it being a reliable substitute for experience.

If this was 12 teams vs 14, then you may have 15-18 QBs going for more than \$1. But this would also apply to other positions and everything would normalize itself.

This process took me a few years to figure out with fine tuning, and mathematically each step makes sense to me. VORP + historical league data + qty of starters are all factored in. Experience will always trump everything and this is only a tool to provide auction estimates. For me, it helps in exploiting where value lies for each position, which is tough to do in auctions.

I really enjoy what I call Rubik’s Cube drafts, where you have to analyze a weird scoring system, preferably with a non-standard budget, and try to maximize your value per dollar.

I recently did one that I am pretty proud of, and I’m curious as to how your system would rate my team, based on what it would predict for positional values. The link to the post detailing the draft is below; tell me what other information you would need to plug into your system, and then see how my draft spending corresponds to the predictions of your system.

Looks very interesting and looking forward to testing it out. Only two things needed:

1. Points per seasons or points per game for positional ADP that you would like to use as benchmark
2. At which ADP for each position do we see the first \$1 (to calculate VORP)

Not sure I understand what you want here–last year’s total points (or points per game) under our scoring system for every player drafted over \$1?

This is starting to sound like work.

I’ll try to put the draft results into a spreadsheet and sort it.

Yes, last year’s or historical data for total points or ppg (using your scoring system) for every player over \$1. This will provide the VORP, which is the crux of the system.

Here you go…

\$\$\$ RANK NAME/TEAM POS \$\$\$ 2022 PTS
1 Patrick Mahomes KC QB 58 736.40
2 Justin Fields Chi QB 49 541.98
3 Jalen Hurts Phi QB 48 714.04
4 Josh Allen Buf QB 48 710.52
5 Lamar Jackson Bal QB 48 442.08
6 Joe Burrow Cin QB 48 679.70
7 Justin Herbert LAC QB 48 613.26
8 Trevor Lawrence Jax QB 38 579.62
9 Deshaun Watson Cle QB 36 154.58
10 Daniel Jones NYG QB 34 585.00
11 Jared Goff Det QB 31 514.32
12 Tua Tagovailoa Mia QB 30 392.92
13 Geno Smith Sea QB 28 622.88
14 Russell Wilson Den QB 27 397.76
15 Dak Prescott Dal QB 26 389.60
16 Aaron Rodgers NYJ QB 25 451.20
17 Kirk Cousins Min QB 24 562.58
18 Jordan Love GB QB 18 19.70
19 Derek Carr NO QB 13 367.08
20 Matthew Stafford LAR QB 12 245.38
21 Trey Lance SF QB 11 28.46
22 Anthony Richardson Ind QB 10 0.00
23 Sam Howell Was QB 7 27.26
24 Jimmy Garoppolo LV QB 6 302.78
25 C.J. Stroud Hou QB 5 0.00
26 Kyler Murray Ari QB 4 409.52
27 Kenny Pickett Pit QB 3 317.86
28 Bryce Young Car QB 3 0.00
29 Ryan Tannehill Ten QB 2 305.24
30 Desmond Ridder Atl QB 2 89.72
1 Christian McCaffrey SF RB 54 606.36
2 Derrick Henry Ten RB 46 673.76
3 Austin Ekeler LAC RB 44 586.70
4 Bijan Robinson Atl RB 41 0.00
5 Nick Chubb Cle RB 40 592.40
6 Saquon Barkley NYG RB 37 587.00
7 Jonathan Taylor Ind RB 36 348.40
8 Josh Jacobs LV RB 33 683.30
9 Rhamondre Stevenson NE RB 33 467.10
10 Tony Pollard Dal RB 32 445.80
11 Breece Hall NYJ RB 30 199.10
12 Travis Etienne Jr. Jax RB 22 438.10
13 Dalvin Cook Min RB 18 511.80
14 Najee Harris Pit RB 16 501.46
15 Cam Akers LAR RB 14 336.30
16 Jahmyr Gibbs Det RB 14 0.00
17 Miles Sanders Car RB 12 482.70
18 Joe Mixon Cin RB 12 452.50
19 Aaron Jones GB RB 11 472.60
20 Dameon Pierce Hou RB 11 392.40
21 Alvin Kamara NO RB 10 444.70
22 Kenneth Walker III Sea RB 9 437.50
23 James Cook Buf RB 9 196.70
24 J.K. Dobbins Bal RB 9 175.20
25 James Conner Ari RB 8 386.20
26 D’Andre Swift Phi RB 7 291.10
27 Alexander Mattison Min RB 7 162.40
28 Isiah Pacheco KC RB 6 310.00
29 Khalil Herbert Chi RB 6 249.80
30 Rashaad Penny Phi RB 6 112.20
31 Javonte Williams Den RB 6 91.00
32 Antonio Gibson Was RB 4 314.90
33 Rachaad White TB RB 4 275.10
34 David Montgomery Det RB 3 383.70
35 AJ Dillon GB RB 3 353.60
36 Brian Robinson Was RB 3 318.70
37 Samaje Perine Den RB 3 238.10
38 Zach Charbonnet Sea RB 3 0.00
39 Tyler Allgeier Atl RB 2 371.40
40 Damien Harris Buf RB 2 196.90
41 Elijah Mitchell SF RB 2 88.60
1 Travis Kelce KC TE 36 326.30
2 Mark Andrews Bal TE 16 196.50
3 T.J. Hockenson Min TE 14 221.40
4 George Kittle SF TE 7 203.50
5 Dallas Goedert Phi TE 5 144.20
6 Darren Waller NYG TE 4 84.80
7 Kyle Pitts Atl TE 4 75.60
8 Evan Engram Jax TE 3 182.90
9 David Njoku Cle TE 3 146.00
10 Tyler Higbee LAR TE 2 152.00
11 Pat Freiermuth Pit TE 2 148.20
12 Mike Gesicki NE TE 2 98.20
1 Ja’Marr Chase Cin WR 40 255.40
2 Justin Jefferson Min WR 38 395.66
3 Tyreek Hill Mia WR 36 369.20
4 Stefon Diggs Buf WR 27 324.60
5 A.J. Brown Phi WR 26 314.60
6 Cooper Kupp LAR WR 26 216.40
7 Amon-Ra St. Brown Det WR 21 280.60
8 Davante Adams LV WR 20 350.50
9 CeeDee Lamb Dal WR 20 318.60
10 Garrett Wilson NYJ WR 17 225.70
11 Allen Lazard NYJ WR 14 177.80
12 Jaylen Waddle Mia WR 11 272.20
13 DeVonta Smith Phi WR 11 263.60
14 DK Metcalf Sea WR 10 232.80
15 Calvin Ridley Jax WR 10 0.00
16 Deebo Samuel SF WR 9 215.40
17 Jerry Jeudy Den WR 9 213.20
18 DJ Moore Chi WR 8 214.10
19 Chris Olave NO WR 8 205.20
20 DeAndre Hopkins Ten WR 8 157.70
21 Tee Higgins Cin WR 7 224.90
22 Keenan Allen LAC WR 7 169.00
23 Mike Evans TB WR 6 233.40
24 Tyler Lockett Sea WR 5 244.30
25 Michael Pittman Jr. Ind WR 5 223.50
26 Amari Cooper Cle WR 4 251.00
27 Terry McLaurin Was WR 4 240.00
28 Brandon Aiyuk SF WR 4 232.80
29 Chris Godwin TB WR 3 231.80
30 Mike Williams LAC WR 3 180.50
31 Gabe Davis Buf WR 3 176.60
32 Jameson Williams Det WR 3 16.10
33 Odell Beckham Jr. Bal WR 3 0.00
34 Christian Watson GB WR 2 174.10
35 George Pickens Pit WR 2 170.50

Thank you for sharing this. However, the data from last year will be needed for the positions. The name of the players are not important, but the points for each of the adps of each position is required. For example, currently for the QB9 slot, there is no way of knowing what the VORP should be as the 154.58 value is too low due to Watson. What is needed is how many points did the nineth best QB score last year.

Ah, ok, I wondered how you handled that.

So how many places do I need to go down–the same as the number of players drafted for \$2 and up at each position?

I will presume yes…

RANK POS 2022 FPTS
1 QB 736.40
2 QB 714.04
3 QB 710.52
4 QB 679.70
5 QB 622.88
6 QB 613.26
7 QB 585.00
8 QB 579.62
9 QB 578.66
10 QB 562.58
11 QB 541.98
12 QB 514.32
13 QB 451.20
14 QB 442.08
15 QB 409.52
16 QB 397.76
17 QB 392.92
18 QB 389.60
19 QB 368.08
20 QB 367.08
21 QB 364.56
22 QB 344.24
23 QB 341.52
24 QB 335.62
25 QB 317.86
26 QB 305.24
27 QB 302.78
28 QB 245.38
29 QB 231.42
30 QB 222.96
1 RB 683.30
2 RB 673.76
3 RB 606.36
4 RB 592.40
5 RB 587.00
6 RB 586.70
7 RB 511.80
8 RB 501.46
9 RB 493.90
10 RB 482.70
11 RB 472.60
12 RB 467.10
13 RB 452.50
14 RB 445.80
15 RB 444.70
16 RB 438.10
17 RB 437.50
18 RB 416.80
19 RB 416.10
20 RB 392.40
21 RB 386.20
22 RB 383.70
23 RB 371.40
24 RB 361.90
25 RB 353.60
26 RB 353.30
27 RB 348.40
28 RB 341.00
29 RB 340.50
30 RB 336.30
31 RB 327.20
32 RB 318.70
33 RB 314.90
34 RB 310.00
35 RB 302.70
36 RB 291.10
37 RB 275.10
38 RB 271.30
39 RB 249.80
40 RB 249.80
41 RB 242.00
1 TE 326.30
2 TE 249.80
3 TE 221.40
4 TE 203.50
5 TE 196.50
6 TE 182.90
7 TE 152.00
8 TE 149.30
9 TE 148.20
10 TE 146.00
11 TE 144.70
12 TE 144.20
1 WR 395.66
2 WR 369.20
3 WR 350.50
4 WR 324.60
5 WR 318.60
6 WR 314.60
7 WR 280.60
8 WR 272.20
9 WR 263.60
10 WR 255.40
11 WR 251.00
12 WR 250.90
13 WR 244.30
14 WR 240.00
15 WR 233.40
16 WR 232.80
17 WR 232.80
18 WR 231.80
19 WR 225.70
20 WR 224.90
21 WR 223.50
22 WR 216.40
23 WR 216.30
24 WR 215.40
25 WR 214.10
26 WR 213.20
27 WR 205.20
28 WR 204.10
29 WR 191.30
30 WR 187.70
31 WR 187.30
32 WR 185.60
33 WR 181.00
34 WR 180.50
35 WR 177.80

These are the full results:

%VORP: QB = 42%, RB = 41%, WR = 14%, TE = 3%

Normalizing this data to the assumption of starting 2QB / 3 RB/ 2 WR/ 1 TE along with dedicating \$190 to this construction, yields:

% auction dollars: QB = 35%, RB = 52%, WR = 12%, TE = 1%

Using the auction dollars that you provided, these are full results:

QB AAV PTS VORP %V RV DIF RB AAV PTS VORP %V RV DIF WR AAV PTS VORP %V RV DIF TE AAV PTS VORP %V RV DIF
1 58 736.4 513.4 7% 49.0 -9.0 1 54 683.3 441.3 6% 63.2 9.2 1 40 395.66 217.86 10% 20.8 -19.2 1 36 326.3 182.1 34% 8.7 -27.3
2 49 714.04 491.1 7% 46.9 -2.1 2 46 673.76 431.8 6% 61.8 15.8 2 38 369.2 191.4 8% 18.3 -19.7 2 16 249.8 105.6 20% 5.0 -11.0
3 48 710.52 487.6 7% 46.5 -1.5 3 44 606.36 364.4 5% 52.2 8.2 3 36 350.5 172.7 8% 16.5 -19.5 3 14 221.4 77.2 14% 3.7 -10.3
4 48 679.7 456.7 7% 43.6 -4.4 4 41 592.4 350.4 5% 50.2 9.2 4 27 324.6 146.8 6% 14.0 -13.0 4 7 203.5 59.3 11% 2.8 -4.2
5 48 622.88 399.9 6% 38.2 -9.8 5 40 587 345.0 5% 49.4 9.4 5 26 318.6 140.8 6% 13.4 -12.6 5 5 196.5 52.3 10% 2.5 -2.5
6 48 613.26 390.3 6% 37.3 -10.7 6 37 586.7 344.7 5% 49.4 12.4 6 26 314.6 136.8 6% 13.1 -12.9 6 4 182.9 38.7 7% 1.8 -2.2
7 48 585 362.0 5% 34.6 -13.4 7 36 511.8 269.8 4% 38.6 2.6 7 21 280.6 102.8 4% 9.8 -11.2 7 4 152 7.8 1% 0.4 -3.6
8 38 579.62 356.7 5% 34.1 -3.9 8 33 501.46 259.5 4% 37.2 4.2 8 20 272.2 94.4 4% 9.0 -11.0 8 3 149.3 5.1 1% 0.2 -2.8
9 36 578.66 355.7 5% 34.0 -2.0 9 33 493.9 251.9 4% 36.1 3.1 9 20 263.6 85.8 4% 8.2 -11.8 9 3 148.2 4.0 1% 0.2 -2.8
10 34 562.58 339.6 5% 32.4 -1.6 10 32 482.7 240.7 3% 34.5 2.5 10 17 255.4 77.6 3% 7.4 -9.6 10 2 146 1.8 0% 0.1 -1.9
11 31 541.98 319.0 5% 30.5 -0.5 11 30 472.6 230.6 3% 33.0 3.0 11 14 251 73.2 3% 7.0 -7.0 11 2 144.7 0.5 0% 0.0 -2.0
12 30 514.32 291.4 4% 27.8 -2.2 12 22 467.1 225.1 3% 32.2 10.2 12 11 250.9 73.1 3% 7.0 -4.0 12 2 144.2 - 0% 0.0 -2.0
13 28 451.2 228.2 3% 21.8 -6.2 13 18 452.5 210.5 3% 30.1 12.1 13 11 244.3 66.5 3% 6.3 -4.7
14 27 442.08 219.1 3% 20.9 -6.1 14 16 445.8 203.8 3% 29.2 13.2 14 10 240 62.2 3% 5.9 -4.1
15 26 409.52 186.6 3% 17.8 -8.2 15 14 444.7 202.7 3% 29.0 15.0 15 10 233.4 55.6 2% 5.3 -4.7
16 25 397.76 174.8 3% 16.7 -8.3 16 14 438.1 196.1 3% 28.1 14.1 16 9 232.8 55 2% 5.3 -3.7
17 24 392.92 170.0 2% 16.2 -7.8 17 12 437.5 195.5 3% 28.0 16.0 17 9 232.8 55 2% 5.3 -3.7
18 18 389.6 166.6 2% 15.9 -2.1 18 12 416.8 174.8 3% 25.0 13.0 18 8 231.8 54 2% 5.2 -2.8
19 13 368.08 145.1 2% 13.9 0.9 19 11 416.1 174.1 3% 24.9 13.9 19 8 225.7 47.9 2% 4.6 -3.4
20 12 367.08 144.1 2% 13.8 1.8 20 11 392.4 150.4 2% 21.5 10.5 20 8 224.9 47.1 2% 4.5 -3.5
21 11 364.56 141.6 2% 13.5 2.5 21 10 386.2 144.2 2% 20.7 10.7 21 7 223.5 45.7 2% 4.4 -2.6
22 10 344.24 121.3 2% 11.6 1.6 22 9 383.7 141.7 2% 20.3 11.3 22 7 216.4 38.6 2% 3.7 -3.3
23 7 341.52 118.6 2% 11.3 4.3 23 9 371.4 129.4 2% 18.5 9.5 23 6 216.3 38.5 2% 3.7 -2.3
24 6 335.62 112.7 2% 10.8 4.8 24 9 361.9 119.9 2% 17.2 8.2 24 5 215.4 37.6 2% 3.6 -1.4
25 5 317.86 94.9 1% 9.1 4.1 25 8 353.6 111.6 2% 16.0 8.0 25 5 214.1 36.3 2% 3.5 -1.5
26 4 305.24 82.3 1% 7.9 3.9 26 7 353.3 111.3 2% 15.9 8.9 26 4 213.2 35.4 2% 3.4 -0.6
27 3 302.78 79.8 1% 7.6 4.6 27 7 348.4 106.4 2% 15.2 8.2 27 4 205.2 27.4 1% 2.6 -1.4
28 3 245.38 22.4 0% 2.1 -0.9 28 6 341 99.0 1% 14.2 8.2 28 4 204.1 26.3 1% 2.5 -1.5
29 2 231.42 8.5 0% 0.8 -1.2 29 6 340.5 98.5 1% 14.1 8.1 29 3 191.3 13.5 1% 1.3 -1.7
30 2 222.96 - 0% 0.0 -2.0 30 6 336.3 94.3 1% 13.5 7.5 30 3 187.7 9.9 0% 0.9 -2.1
31 6 327.2 85.2 1% 12.2 6.2 31 3 187.3 9.5 0% 0.9 -2.1
32 4 318.7 76.7 1% 11.0 7.0 32 3 185.6 7.8 0% 0.7 -2.3
33 4 314.9 72.9 1% 10.4 6.4 33 3 181 3.2 0% 0.3 -2.7
34 3 310 68.0 1% 9.7 6.7 34 2 180.5 2.7 0% 0.3 -1.7
35 3 302.7 60.7 1% 8.7 5.7 35 2 177.8 0 0% 0.0 -2.0
36 3 291.1 49.1 1% 7.0 4.0
37 3 275.1 33.1 0% 4.7 1.7
38 3 271.3 29.3 0% 4.2 1.2
39 2 249.8 7.8 0% 1.1 -0.9
40 2 249.8 7.8 0% 1.1 -0.9
41 2 242 - 0% 0.0 -2.0

%V = percentage of positional VORP for player
RV = real value in \$
DIF = difference between RV - AAV

The ideal spend using \$190 is 2QB = \$66.6, 3RB = \$99.0, 2WR = \$22.7, 1TE = \$1.9

It’s axually 2RB/2WR/1Flex, but close enough.

If I can make any sense of those numbers, it looks like your formula says that everyone overspent on QBs, WRs and TEs, and underspent on RBs. I’m just not sure what that means in terms of useful drafting information.

Seems like it just says that RBs are the most valuable players in this scoring system, which I already knew when I drafted Bijan Robinson, Derrick Henry, and Jonathan Taylor.

I spent \$62 on 2 QBs, \$123 on 3 RBs, \$5 on 2 WR, and \$1 on a TE; so that was \$191.

New data basing on QB / 3RB/ 2Wr/ 1TE setup. The main purpose is to gage where value lies within a specific league’s draft based on historical VORP and auction dollars. For your league settings, the results are common sense obvious and this just validates it while providing context for each ADP.

AAV PTS VORP %V RV DIF RB AAV PTS VORP %V RV DIF WR AAV PTS VORP %V RV DIF TE AAV PTS VORP %V RV DIF
58 736.4 513.4 7% 29.7 -28.3 1 54 683.3 441.3 6% 76.6 22.6 1 40 395.66 217.86 10% 25.2 -14.8 1 36 326.3 182.1 34% 10.5 -25.5
49 714.04 491.1 7% 28.4 -20.6 2 46 673.76 431.8 6% 75.0 29.0 2 38 369.2 191.4 8% 22.2 -15.8 2 16 249.8 105.6 20% 6.1 -9.9
48 710.52 487.6 7% 28.2 -19.8 3 44 606.36 364.4 5% 63.3 19.3 3 36 350.5 172.7 8% 20.0 -16.0 3 14 221.4 77.2 14% 4.5 -9.5
48 679.7 456.7 7% 26.4 -21.6 4 41 592.4 350.4 5% 60.9 19.9 4 27 324.6 146.8 6% 17.0 -10.0 4 7 203.5 59.3 11% 3.4 -3.6
48 622.88 399.9 6% 23.2 -24.8 5 40 587 345.0 5% 59.9 19.9 5 26 318.6 140.8 6% 16.3 -9.7 5 5 196.5 52.3 10% 3.0 -2.0
48 613.26 390.3 6% 22.6 -25.4 6 37 586.7 344.7 5% 59.9 22.9 6 26 314.6 136.8 6% 15.8 -10.2 6 4 182.9 38.7 7% 2.2 -1.8
48 585 362.0 5% 21.0 -27.0 7 36 511.8 269.8 4% 46.9 10.9 7 21 280.6 102.8 4% 11.9 -9.1 7 4 152 7.8 1% 0.5 -3.5
38 579.62 356.7 5% 20.6 -17.4 8 33 501.46 259.5 4% 45.1 12.1 8 20 272.2 94.4 4% 10.9 -9.1 8 3 149.3 5.1 1% 0.3 -2.7
36 578.66 355.7 5% 20.6 -15.4 9 33 493.9 251.9 4% 43.7 10.7 9 20 263.6 85.8 4% 9.9 -10.1 9 3 148.2 4.0 1% 0.2 -2.8
34 562.58 339.6 5% 19.7 -14.3 10 32 482.7 240.7 3% 41.8 9.8 10 17 255.4 77.6 3% 9.0 -8.0 10 2 146 1.8 0% 0.1 -1.9
31 541.98 319.0 5% 18.5 -12.5 11 30 472.6 230.6 3% 40.0 10.0 11 14 251 73.2 3% 8.5 -5.5 11 2 144.7 0.5 0% 0.0 -2.0
30 514.32 291.4 4% 16.9 -13.1 12 22 467.1 225.1 3% 39.1 17.1 12 11 250.9 73.1 3% 8.5 -2.5 12 2 144.2 - 0% 0.0 -2.0
28 451.2 228.2 3% 13.2 -14.8 13 18 452.5 210.5 3% 36.6 18.6 13 11 244.3 66.5 3% 7.7 -3.3
27 442.08 219.1 3% 12.7 -14.3 14 16 445.8 203.8 3% 35.4 19.4 14 10 240 62.2 3% 7.2 -2.8
26 409.52 186.6 3% 10.8 -15.2 15 14 444.7 202.7 3% 35.2 21.2 15 10 233.4 55.6 2% 6.4 -3.6
25 397.76 174.8 3% 10.1 -14.9 16 14 438.1 196.1 3% 34.1 20.1 16 9 232.8 55 2% 6.4 -2.6
24 392.92 170.0 2% 9.8 -14.2 17 12 437.5 195.5 3% 34.0 22.0 17 9 232.8 55 2% 6.4 -2.6
18 389.6 166.6 2% 9.6 -8.4 18 12 416.8 174.8 3% 30.4 18.4 18 8 231.8 54 2% 6.3 -1.7
13 368.08 145.1 2% 8.4 -4.6 19 11 416.1 174.1 3% 30.2 19.2 19 8 225.7 47.9 2% 5.5 -2.5
12 367.08 144.1 2% 8.3 -3.7 20 11 392.4 150.4 2% 26.1 15.1 20 8 224.9 47.1 2% 5.5 -2.5
11 364.56 141.6 2% 8.2 -2.8 21 10 386.2 144.2 2% 25.0 15.0 21 7 223.5 45.7 2% 5.3 -1.7
10 344.24 121.3 2% 7.0 -3.0 22 9 383.7 141.7 2% 24.6 15.6 22 7 216.4 38.6 2% 4.5 -2.5
7 341.52 118.6 2% 6.9 -0.1 23 9 371.4 129.4 2% 22.5 13.5 23 6 216.3 38.5 2% 4.5 -1.5
6 335.62 112.7 2% 6.5 0.5 24 9 361.9 119.9 2% 20.8 11.8 24 5 215.4 37.6 2% 4.4 -0.6
5 317.86 94.9 1% 5.5 0.5 25 8 353.6 111.6 2% 19.4 11.4 25 5 214.1 36.3 2% 4.2 -0.8
4 305.24 82.3 1% 4.8 0.8 26 7 353.3 111.3 2% 19.3 12.3 26 4 213.2 35.4 2% 4.1 0.1
3 302.78 79.8 1% 4.6 1.6 27 7 348.4 106.4 2% 18.5 11.5 27 4 205.2 27.4 1% 3.2 -0.8
3 245.38 22.4 0% 1.3 -1.7 28 6 341 99.0 1% 17.2 11.2 28 4 204.1 26.3 1% 3.0 -1.0
2 231.42 8.5 0% 0.5 -1.5 29 6 340.5 98.5 1% 17.1 11.1 29 3 191.3 13.5 1% 1.6 -1.4
2 222.96 - 0% 0.0 -2.0 30 6 336.3 94.3 1% 16.4 10.4 30 3 187.7 9.9 0% 1.1 -1.9
31 6 327.2 85.2 1% 14.8 8.8 31 3 187.3 9.5 0% 1.1 -1.9
32 4 318.7 76.7 1% 13.3 9.3 32 3 185.6 7.8 0% 0.9 -2.1
33 4 314.9 72.9 1% 12.7 8.7 33 3 181 3.2 0% 0.4 -2.6
34 3 310 68.0 1% 11.8 8.8 34 2 180.5 2.7 0% 0.3 -1.7
35 3 302.7 60.7 1% 10.5 7.5 35 2 177.8 0 0% 0.0 -2.0
36 3 291.1 49.1 1% 8.5 5.5
37 3 275.1 33.1 0% 5.7 2.7
38 3 271.3 29.3 0% 5.1 2.1
39 2 249.8 7.8 0% 1.4 -0.6
40 2 249.8 7.8 0% 1.4 -0.6
41 2 242 - 0% 0.0 -2.0
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