Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter February 21, 2014

An expectation-based metric for NFL field goal kickers

  • R. Drew Pasteur EMAIL logo and Kyle Cunningham-Rhoads

Abstract

The standard metric for American football field goal kickers is simply the percentage of attempts successfully converted. Due to variance in distance of attempts and other conditions (weather, altitude, defense, etc.), we argue that field goal percentage is an insufficient measure of kicker performance. Using three seasons of NFL data, we construct a multivariate logistic regression model for the success probability of a given attempt. This leads naturally to metrics in which a kicker’s performance is compared to model expectations, if a replacement-level player was attempting the same kicks. Player salaries correlate only weakly with our measures of field goal kicking success. We find that those kickers selected to the Pro Bowl and All-Pro teams were rather mediocre by our metrics, over the seasons studied. The relative difficulty of kicking in various stadiums is also considered. Finally, we discuss the degree to which field goal kicking is a skill that can be maintained over multiple seasons.


Corresponding author: R. Drew Pasteur, Mathematics and Computer Science, College of Wooster, 311 Taylor Hall, 1189 Beall Ave. Wooster, OH 44691, USA, Tel.: +330-263-2486, e-mail:

Appendix A

Overall Kicking Results, by PARK (minimum 48 FGA)

Player & YearFGM/AFG%Repl%FG%+PARKFGM dist.
1) R. Bironas81/9387.1% (4)71.1% (4)+16.0% (2)+44.6 (1)37.6 (3)
2) S. Janikowski83/10083.0% (24)68.6% (2)+14.4% (3)+43.1 (2)37.3 (4)
3) D. Akers107/12883.6% (23)74.6% (16)+9.0% (15)+34.5 (3)34.1 (24)
4) J. Hanson54/6484.4% (15)66.7% (1)+17.7% (1)+34.0 (4)40.2 (1)
5) J. Kasay75/8786.2% (8)73.5% (9)+12.7% (4)+33.1 (5)36.4 (7)
6) J. Brown83/9983.8% (20)74.1% (12)+9.8% (11)+29.1 (6)37.6 (2)
7) R. Gould75/8786.2% (8)75.2% (18)+11.0% (7)+28.8 (7)34.9 (14)
8) J. Feely81/9684.4% (15)74.6% (15)+9.8% (10)+28.3 (8)35.4 (13)
9) R. Longwell74/8290.2% (1)78.9% (30)+11.4% (5)+28.0 (9)34.3 (22)
10) N. Rackers74/8587.1% (5)76.2% (21)+10.8% (8)+27.6 (10)34.9 (15)
11) D. Carpenter77/9581.1% (28)71.8% (5)+9.3% (13)+26.5 (11)36.4 (6)
12) J. Reed83/9983.8% (20)75.1% (17)+8.7% (17)+25.9 (12)33.0 (29)
13) O. Mare76/8688.4% (2)79.4% (31)+9.0% (14)+23.2 (13)33.7 (26)
14) J. Nedney57/6785.1% (11)73.8% (10)+11.3% (6)+22.7 (14)35.8 (10)
15) P. Dawson70/8384.3% (17)75.5% (19)+8.8% (16)+21.9 (15)32.9 (30)
16) R. Lindell74/9280.4% (29)72.7% (6)+7.7% (20)+21.2 (16)34.5 (19)
17) N. Kaeding84/10084.0% (18)77.3% (25)+6.7% (22)+20.1 (17)33.5 (28)
18) S. Gostkowski72/8584.7% (14)76.9% (23)+7.8% (19)+19.9 (18)34.3 (21)
19) A. Vinatieri57/6686.4% (7)76.5% (22)+9.8% (9)+19.5 (19)35.8 (9)
20) G. Hartley50/5787.7% (3)78.2% (28)+9.6% (12)+16.4 (20)34.4 (20)
21) S. Graham58/6885.3% (10)77.4% (26)+7.9% (18)+16.2 (21)32.6 (32)
22) M. Prater71/8781.6% (27)75.6% (20)+6.1% (25)+15.8 (22)35.7 (12)
23) M. Bryant67/7984.8% (13)78.3% (29)+6.5% (24)+15.4 (23)33.9 (25)
24) R. Succop45/5581.8% (26)74.2% (14)+7.6% (21)+12.5 (24)34.5 (18)
25) S. Suisham66/8577.6% (31)72.8% (7)+4.9% (28)+12.4 (25)34.8 (16)
26) M. Crosby80/10476.9% (33)73.0% (8)+3.9% (30)+12.1 (26)34.1 (23)
27) B. Cundiff51/5986.4% (6)79.9% (32)+6.5% (23)+11.6 (27)31.4 (35)
28) C. Barth47/5979.7% (30)74.1% (13)+5.5% (27)+9.8 (28)35.9 (8)
29) M. Stover46/5583.6% (22)78.0% (27)+5.6% (26)+9.3 (29)31.9 (33)
30) J. Carney56/6684.8% (12)80.4% (34)+4.5% (29)+8.9 (30)31.9 (34)
31) J. Scobee59/8172.8% (35)69.8% (3)+3.0% (31)+7.3 (31)37.3 (5)
32) N. Folk70/9276.1% (34)73.9% (11)+2.2% (34)+6.0 (32)35.7 (11)
33) L. Tynes47/5683.9% (19)81.2% (35)+2.8% (32)+4.6 (33)32.7 (31)
34) J. Elam42/5182.4% (25)80.1% (33)+2.2% (33)+3.4 (34)34.6 (17)
35) K. Brown54/7077.1% (32)77.0% (24)+0.2% (35)+0.3 (35)33.6 (27)
Appendix B

Seasonal Kicking Results, by PARK (minimum 16 FGA)

Player & YearFGM/AFG%Repl%FG%+PARKFGM dist.
1) S. Janikowski ’0926/2989.7% (14)61.6% (1)+28.1% (2)+24.4 (1)42.5 (2)
2) J. Hanson ’0821/2295.5% (1)64.8% (3)+30.7% (1)+20.3 (2)42.9 (1)
3) R. Bironas ’0830/3585.7% (32)68.4% (8)+17.3% (6)+18.2 (3)37.7 (11)
4) J. Reed ’0832/3688.9% (18)72.7% (30)+16.2% (8)+17.5 (4)33.3 (70)
5) J. Brown ’0831/3686.1% (31)70.9% (16)+15.2% (14)+16.4 (5)39.0 (8)
6) S. Gostkowski ’0836/4090.0% (12)76.7% (59)+13.3% (22)+16.0 (6)34.4 (56)
7) R. Bironas ’0927/3284.4% (45)68.4% (9)+16.0% (9)+15.3 (7)39.1 (7)
8) A. Vinatieri ’1029/3193.5% (4)77.2% (64)+16.4% (7)+15.2 (8)35.9 (30)
9) J. Kasay ’0828/3190.3% (10)74.5% (43)+15.8% (10)+14.7 (9)36.7 (22)
10) J. Carney ’0838/4388.4% (22)77.1% (63)+11.3% (35)+14.6 (10)32.3 (86)
11) N. Folk ’0820/2290.9% (8)69.3% (11)+21.6% (3)+14.3 (11)40.8 (4)
12) J. Kasay ’1025/2986.2% (29)70.6% (14)+15.6% (11)+13.6 (12)37.6 (12)
13) D. Akers ’0842/5084.0% (49)75.0% (48)+9.0% (47)+13.4 (13)33.6 (66)
14) D. Akers ’0932/3786.5% (28)74.6% (44)+11.8% (29)+13.1 (14)34.8 (46)
15) J. Elam ’0830/3293.8% (3)80.1% (84)+13.6% (20)+13.1 (15)34.6 (50)
16) J. Nedney ’0829/3387.9% (23)74.8% (47)+13.1% (24)+13.0 (16)35.7 (35)
17) J. Feely ’1024/2788.9% (18)73.3% (32)+15.6% (12)+12.6 (17)36.6 (26)
18) K. Brown ’0829/3387.9% (23)75.4% (50)+12.5% (26)+12.3 (18)34.6 (51)
19) R. Succop ’0925/2986.2% (29)72.5% (29)+13.7% (19)+11.9 (19)34.0 (59)
20) P. Dawson ’0830/3683.3% (51)72.3% (28)+11.0% (37)+11.9 (20)34.6 (52)
21) N. Rackers ’1027/3090.0% (12)76.8% (60)+13.2% (23)+11.9 (21)35.9 (31)
22) R. Longwell ’0829/3485.3% (37)73.7% (36)+11.6% (31)+11.8 (22)37.1 (17)
23) R. Gould ’0924/2885.7% (32)71.7% (24)+14.0% (16)+11.8 (23)35.7 (33)
24) J. Feely ’0933/4180.5% (65)71.0% (17)+9.5% (42)+11.7 (24)36.8 (19)
25) N. Rackers ’0830/3585.7% (32)74.7% (45)+11.0% (38)+11.6 (25)34.0 (60)
26) B. Cundiff ’1030/3390.9% (8)79.2% (78)+11.7% (30)+11.6 (26)32.5 (80)
27) S. Janikowski ’1033/4180.5% (65)71.2% (19)+9.3% (45)+11.4 (27)36.3 (28)
28) R. Bironas ’1024/2692.3% (6)78.1% (70)+14.3% (15)+11.1 (28)35.7 (33)
29) R. Gould ’0826/2989.7% (14)77.0% (62)+12.6% (25)+11.0 (29)35.5 (37)
30) R. Longwell ’0928/3093.3% (5)81.2% (92)+12.1% (28)+10.9 (30)33.1 (74)
31) M. Bryant ’1028/3190.3% (10)78.8% (75)+11.5% (34)+10.7 (31)34.5 (54)
32) S. Suisham ’1017/2085.0% (39)67.5% (7)+17.5% (5)+10.5 (32)34.9 (45)
33) O. Mare ’0824/2788.9% (18)76.4% (57)+12.5% (27)+10.1 (33)36.7 (23)
34) S. Graham ’0821/2487.5% (25)73.6% (35)+13.9% (17)+10.0 (34)33.6 (66)
35) D. Carpenter ’0925/2889.3% (17)77.7% (67)+11.6% (32)+9.7 (35)35.2 (40)
36) M. Prater ’1016/1888.9% (18)71.1% (18)+17.8% (4)+9.6 (36)38.0 (10)
37) J. Reed ’0927/3187.1% (27)77.3% (65)+9.8% (40)+9.1 (37)33.2 (73)
38) R. Lindell ’0928/3384.8% (41)75.7% (52)+9.1% (46)+9.0 (38)32.7 (78)
39) O. Mare ’0924/2692.3% (6)80.8% (88)+11.5% (33)+9.0 (39)33.4 (69)
40) D. Carpenter ’0822/2684.6% (43)73.5% (33)+11.1% (36)+8.7 (40)37.3 (14)
41) R. Lindell ’0830/3878.9% (72)71.4% (20)+7.6% (50)+8.6 (41)36.4 (27)
42) D. Carpenter ’1030/4173.2% (88)66.6% (5)+6.6% (55)+8.1 (42)36.8 (21)
43) D. Akers ’1033/4180.5% (65)74.1% (39)+6.4% (59)+7.9 (43)34.0 (60)
44) P. Dawson ’0917/1989.5% (16)76.0% (55)+13.5% (21)+7.7 (44)33.7 (65)
45) J. Brown ’0919/2479.2% (71)68.5% (10)+10.7% (39)+7.7 (45)39.1 (6)
46) G. Hartley ’0914/1687.5% (25)71.9% (25)+15.6% (13)+7.5 (46)35.4 (39)
47) N. Kaeding ’0932/3884.2% (47)77.7% (68)+6.5% (56)+7.4 (47)32.2 (87)
48) S. Janikowski ’0824/3080.0% (68)72.0% (26)+8.0% (49)+7.2 (48)33.0 (76)
49) J. Scobee ’1022/2878.6% (74)70.2% (12)+8.3% (48)+7.0 (49)38.2 (9)
50) M. Crosby ’0827/3479.4% (69)72.8% (31)+6.6% (54)+6.8 (50)35.1 (41)
51) D. Rayner ’1013/1681.3% (60)67.4% (6)+13.9% (18)+6.7 (51)39.8 (5)
52) N. Kaeding ’0829/3485.3% (37)78.9% (76)+6.4% (57)+6.5 (52)32.4 (82)
53) N. Kaeding ’1023/2882.1% (55)74.8% (46)+7.4% (51)+6.2 (53)36.8 (20)
54) M. Prater ’0930/3585.7% (32)79.8% (82)+5.9% (63)+6.2 (54)33.8 (64)
55) M. Stover ’0831/3783.8% (50)78.3% (74)+5.5% (65)+6.1 (55)31.7 (88)
56) R. Gould ’1025/3083.3% (51)76.6% (58)+6.7% (53)+6.1 (56)33.4 (68)
57) J. Nedney ’0917/2181.0% (62)71.6% (22)+9.4% (43)+5.9 (57)37.2 (15)
58) M. Crosby ’1025/3278.1% (75)72.0% (27)+6.1% (60)+5.8 (58)34.9 (43)
59) C. Barth ’0914/1973.7% (85)64.3% (2)+9.3% (44)+5.3 (59)41.6 (3)
60) M. Bryant ’0832/3884.2% (47)79.5% (80)+4.7% (68)+5.3 (60)33.2 (72)
61) R. Longwell ’1017/1894.4% (2)84.8% (94)+9.6% (41)+5.2 (61)31.4 (91)
62) A. Vinatieri ’0821/2680.8% (63)74.4% (42)+6.4% (58)+5.0 (62)37.2 (16)
63) J. Brown ’1033/3984.6% (43)80.4% (85)+4.2% (72)+4.9 (63)35.5 (38)
64) J. Kasay ’0922/2781.5% (59)75.6% (51)+5.9% (62)+4.8 (64)34.5 (55)
65) S. Gostkowski ’0926/3281.3% (60)76.8% (61)+4.4% (69)+4.3 (65)34.5 (53)
66) N. Rackers ’0917/2085.0% (39)78.1% (71)+6.9% (52)+4.1 (66)34.8 (48)
67) O. Mare ’1028/3384.8% (41)80.7% (87)+4.2% (73)+4.1 (67)31.4 (89)
68) J. Feely ’0824/2885.7% (32)81.0% (90)+4.7% (67)+4.0 (68)32.4 (83)
69) J. Hanson ’0921/2875.0% (82)70.6% (15)+4.4% (70)+3.7 (69)36.6 (25)
70) C. Barth ’1023/2882.1% (55)77.8% (69)+4.4% (71)+3.7 (70)34.9 (44)
71) R. Lindell ’1016/2176.2% (79)70.5% (13)+5.7% (64)+3.6 (71)33.9 (62)
72) D. Beuhler ’1024/3275.0% (82)71.6% (23)+3.4% (74)+3.2 (72)36.9 (18)
73) M. Stover ’0915/1883.3% (51)77.4% (66)+5.9% (61)+3.2 (73)32.3 (85)
74) L. Tynes ’0927/3284.4% (45)81.1% (91)+3.2% (75)+3.1 (74)34.1 (58)
75) M. Nugent ’1015/1978.9% (72)74.0% (37)+5.0% (66)+2.8 (75)33.8 (63)
76) P. Dawson ’1023/2882.1% (55)79.4% (79)+2.7% (76)+2.3 (76)30.2 (93)
77) G. Hartley ’1023/2882.1% (55)79.6% (81)+2.6% (77)+2.2 (77)34.8 (47)
78) J. Scobee ’0819/2576.0% (81)74.1% (38)+1.9% (78)+1.5 (78)37.6 (13)
79) N. Folk ’1032/4276.2% (79)75.1% (49)+1.1% (81)+1.3 (79)33.3 (70)
80) L. Tynes ’1019/2382.6% (54)80.7% (86)+1.9% (79)+1.3 (80)31.4 (90)
81) S. Suisham ’0923/2979.3% (70)78.1% (72)+1.2% (80)+1.0 (81)33.1 (75)
82) S. Suisham ’0826/3672.2% (89)71.4% (21)+0.8% (82)+0.8 (82)36.3 (29)
83) R. Succop ’1020/2676.9% (76)76.2% (56)+0.7% (83)+0.6 (83)35.1 (42)
84) B. Cundiff ’0921/2680.8% (63)80.8% (89)0.0% (84)0.0 (84)29.9 (94)
85) M. Prater ’0825/3473.5% (87)73.5% (34)0.0% (85)0.0 (85)36.6 (24)
86) M. Crosby ’0928/3873.7% (85)74.1% (40)–0.4% (86)–0.5 (86)32.4 (84)
87) J. Reed ’1024/3275.0% (82)75.8% (54)–0.8% (87)–0.7 (87)32.5 (79)
88) J. Scobee ’0918/2864.3% (92)65.7% (4)–1.4% (88)–1.2 (88)35.8 (32)
89) S. Graham ’0923/3076.7% (77)78.9% (77)–2.2% (89)–2.0 (89)32.8 (77)
90) J. Carney ’0913/1776.5% (78)84.6% (93)–8.1% (91)–4.1 (90)31.2 (92)
91) G. Gano ’1024/3568.6% (90)74.2% (41)–+5.6% (90)–5.9 (91)35.6 (36)
92) N. Folk ’0918/2864.3% (92)75.8% (53)–11.5% (92)–9.6 (92)34.4 (56)
93) J. Elam ’0912/1963.2% (94)80.1% (83)–16.9% (94)–9.6 (93)34.7 (49)
94) K. Brown ’0921/3265.6% (91)78.2% (73)–12.6% (93)–12.1 (94)32.4 (81)
Appendix C

Stadium difficulty, by average yardage adjustment. Raw yardage is relative to a domed stadium with an average defense. Final yardage is relative to the NFL league-wide average value.

StadiumTemp.WindAltitudeDefenseRawFinal
Buffalo+2.11+2.01–0.13+3.98+2.57
Pittsburgh+1.87+1.04+0.99+3.90+2.48
Cleveland+1.98+1.24+0.17+3.38+1.97
Chicago+1.93+1.09+0.15+3.16+1.75
Green Bay+2.02+0.74+0.34+3.10+1.69
Cincinnati+1.25+1.64+0.11+3.00+1.59
Baltimore+1.28+0.49+0.71+2.48+1.06
New England+1.41+0.68+0.32+2.41+1.00
San Francisco+0.67+1.89–0.16+2.40+0.98
Misc. venues+2.22+0.56–0.41+2.38+0.97
New York (old)+1.13+0.96+0.27+2.36+0.94
Philadelphia+1.21+0.79+0.22+2.22+0.80
Kansas City+1.31+1.20–0.66+1.86+0.44
Dallas (old)+0.05+1.82–0.09+1.78+0.36
New York (new)+1.22+0.44+0.10+1.75+0.34
Tennessee+0.79+0.58+0.22+1.59+0.17
Oakland+0.79+1.02–0.32+1.50+0.08
Washington+0.98+0.37+0.05+1.41–0.01
Seattle+1.33+0.49–0.68+1.14–0.27
Carolina+0.86+0.38–0.14+1.10–0.32
San Diego+0.25+0.42+0.17+0.85–0.57
Dallas (new)+0.07+0.54+0.14+0.75–0.67
Tampa Bay–0.27+0.77+0.18+0.69–0.73
Indianapolis +0.22+0.29+0.15+0.65–0.76
Houston+0.17+0.53–0.10+0.60–0.81
Miami–0.48+0.81+0.26+0.59–0.83
Jacksonville+0.07+0.80–0.48+0.38–1.03
New Orleans+0.24+0.24–1.17
Minnesota+0.19+0.19–1.22
Atlanta+0.11+0.11–1.30
St. Louis–0.44–0.44–1.85
Arizona–0.70–0.70–2.12
Detroit–0.89–0.89–2.31
Denver+1.47+0.59–3.15–0.68–1.77–3.19

Domed stadium with convertible roof (sometimes closed). Domed stadium with permanent roof (always closed).

References

Berry, Donald A. and Timothy D. Berry. 1985. “The Probability of a Field Goal: Rating Kickers.” The American Statistician 39(2):152–5.Search in Google Scholar

Berry, Scott and Craig Wood. 2004. “The Cold-Foot Effect.” Chance 17(4):47–51.10.1080/09332480.2004.10554926Search in Google Scholar

Bilder, Christopher R. and Thomas M. Loughin. 1998.“’It’s good!’ An Analysis of the Probability of Success for Placekicks.” Chance 11(2):20–24,30.Search in Google Scholar

Burke, Brian. 2012. “Temperature and Field Goals.” Advanced NFL Stats blog, January 17. (http://www.advancednflstats.com/2012/01/temperature-and-field-goals.html).Search in Google Scholar

Carré, M. J., T. Asai, T. Akatsuka, and S. J. Haake. 2002. “The Curve Kick of a Football II: Flight Through the Air.” Sports Engineering 5:193–200.10.1046/j.1460-2687.2002.00109.xSearch in Google Scholar

Crossman, Matt. 2013. “The Art of Snap, Hold, Kick … it Could Decide the Super Bowl.” Sporting News, January 22. Accessed July 29, 2013 (http://www.sportingnews.com/nfl/story/2013-01-22/super-bowl-2013-ravens-49ers-david-akers-justin-tucker-snap-hold-kick).Search in Google Scholar

Elsayed, Khaled. 2012. “Signature Stat Snapshot: Time to Throw.” Pro Football Focus, November 7. Accessed July 29, 2013 (https://www.profootballfocus.com/blog/2012/11/07/signature-stat-snapshot-time-to-throw/).Search in Google Scholar

Football Outsiders. 2013. “Methods to our Madness.” Accesssed August 2, 2013 (http://www.footballoutsiders.com/info/methods).Search in Google Scholar

Gaines, Cork. 2011. “History of the NFL Salary Cap.” Business Insider, July 20. Accessed August 17, 2013 (http://www.businessinsider.com/nfl-sports-chart-of-the-day-history-nfl-salary-cap-2011-7).Search in Google Scholar

Gaines, Cork. 2012. “This Is One of the Last Times You’ll See Football Being Played on a Baseball Diamond.” Business Insider, September 11. Accessed December 17, 2013 (http://www.businessinsider.com/dirt-infield-nfl-2012-9).Search in Google Scholar

Gay, Timothy. 2004. Football Physics: The Science of the Game. Holtzbrinck Publishers, Stuttgart; Rodale, Inc., Emmaus, Pennsylvania.Search in Google Scholar

Gutierrez, Paul. 2013. “Janikowski Calls Report ‘Ridiculous.’” ESPN, September 13. Accessed December 17, 2013 (http://espn.go.com/nfl/story/_/id/9666591/sebastian-janikowski-oakland-raiders-refutes-report-says-fan-oakland-athletics).Search in Google Scholar

King, Peter. 1992. “The Riddle of the Kicker.” Sports Illustrated, September 7. Accessed December 13, 2012 (http://sportsillustrated.cnn.com/vault/article/magazine/MAG1004197/index.htm).Search in Google Scholar

Maske, Mark. 2011. “NFL Kickers Making Field Goals at Record Pace.” The Washington Post, October 29. Accessed December 13, 2012 (http://articles.washingtonpost.com/2011-10-29/sports/35280534_1_kickers-nick-novak-nick-folk).Search in Google Scholar

McCallum, Jack. 1999. “Just for Kicks.” Sports Illustrated, October 4. Accessed December 13, 2012 (http://sportsillustrated.cnn.com/vault/article/magazine/MAG1017189/1/index.htm).Search in Google Scholar

Meers, Kevin. 2012. “Calculating Wins Added in Football: a First Attempt.” The Harvard College Sports Analysis Collective, January 30. Accessed August 2, 2013 (http://harvardsportsanalysis.wordpress.com/2012/01/30/calculating-wins-added-in-football-a-first-attempt/).Search in Google Scholar

Morrison, Donald G. and Manohar U. Kalwani. 1993. “The Best NFL Field Goal Kickers: Are they Lucky or Good?” Chance 6(3):30–7.10.1080/09332480.1993.10542375Search in Google Scholar

Petti, Bill. 2011. “What Hitting Metrics Correlate Year-to-Year?” Beyond the Box Score, September 1. Accessed August 17, 2013 (http://www.beyondtheboxscore.com/2011/9/1/2393318/what-hitting-metrics-are-consistent-year-to-year).Search in Google Scholar

Picard, Richard R. and R. Dennis Cook. 1984. “Cross-Validation of Regression Models.” Journal of the American Statistical Association 79(387):575–83.10.1080/01621459.1984.10478083Search in Google Scholar

Pro Football Reference. 2012. “Kicking and Punting.” Accessed December 13, 2012 (http://www.pro-football-reference.com/years/).Search in Google Scholar

Romer, David. 2006. “Do Firms Maximize? Evidence from Professional Football.” Journal of Political Economy 114(2):340–65.10.1086/501171Search in Google Scholar

Schatz, Aaron. 2006. “N.F.L. Kickers are Judged on the Wrong Criteria.” The New York Times, November 12. Accessed August 17, 2013 (http://www.nytimes.com/2006/11/12/sports/football/12score.html).Search in Google Scholar

Schlauch, Dan. 2010. “An Analysis of Placekicker Salary Distribution in the NFL.” Advanced NFL Stats Community, September 19. Accessed August 13, 2013 (http://community.advancednflstats.com/2010/09/analysis-of-placekicker-salary.html).Search in Google Scholar

Schuckers, Michael. 2010. “NHL Shootout as Crapshoot.” Accessed February 5, 2013 (http://statsportsconsulting.com/2010/12/21/nhl-shootout-as-crapshoot/).Search in Google Scholar

Stapleton, Arnie. 2010. “NFL Kickers Dramatically Improved Over the Years.” The Washington Times, November 12. Accessed December 13, 2012 (http://www.washingtontimes.com/news/2010/nov/12/nfl-kickers-dramatically-improved-over-the-years/).Search in Google Scholar

USA Today, 2010. “USA Today Salaries Databases.” Accessed August 2, 2013 (http://content.usatoday.com/sportsdata/football/nfl/salaries/team).Search in Google Scholar

Published Online: 2014-2-21
Published in Print: 2014-1-1

©2014 by Walter de Gruyter Berlin/Boston

Downloaded on 9.12.2023 from https://www.degruyter.com/document/doi/10.1515/jqas-2013-0039/html
Scroll to top button