Scott Fish Bowl's Top 100 from 2018

The 2019 Scott Fish Bowl scoring was just released earlier this week. Assuming no changes, here is the scoring breakdown...

Passing:

• 4 point passing TD
• -3 point interception
• -1 point interception for TD
• 1 point for 25 yards passing (.04/per),
• 1 point per 2 point conversions
• .25 points per 1st down

Rushing:

• 6 point rushing TD
• 1 point for 10 yards rushing (.1/per),
• 2 points for 2 point conversions
• .5 point per 1st down

Receiving:

• 6 point receiving TD
• 1 point for 10 yards receiving (.1/per),
• 2 points for 2 point conversions,
• .5 point per 1st down
• .5 point per reception

TE:

• Extra .5 point per first down
• Extra .5 point per reception

Returns:

• 6 point for any return TD
• 6 points if your player recovers a ball in the endzone for a TD

If memory serves, the scoring system is not drastically different than that of 2018, with the exception of the -3 point INT.

First down data is a bit tricky, but thanks to the play query tools at Pro Football Reference we can pull it fairly easily. The down side is that the lower probability events (like pick 6, fumble lost, and punt and kick return TDs) are a lot of trouble to query separately and merge with the player list. So if we neglect the splitting hairs of these low probability events, we can generate the hypothetical scores of the 2018 under this slightly modified scoring system.

Rank, Player, Score

1 Patrick Mahomes  476.58
2 Matt Ryan  421.71
3 Deshaun Watson  396.26
4 Ben Roethlisberger  388.89
5 Todd Gurley  387.60
6 Christian McCaffrey  382.75
7 Saquon Barkley  380.80
8 Andrew Luck  376.49
9 Aaron Rodgers  370.83
10 Jared Goff  365.32
11 Travis Kelce  364.60
12 Drew Brees  364.43
13 Alvin Kamara  352.90
14 Russell Wilson  347.85
15 Dak Prescott  344.34
16 Zach Ertz  343.80
17 Kirk Cousins  341.92
18 Ezekiel Elliott  341.60
19 Cam Newton  330.40
20 Tom Brady  330.31
21 DeAndre Hopkins  320.50
22 Tyreek Hill  320.30
23  Philip Rivers  319.52
24 George Kittle  318.20
25 Julio Jones  313.90
26 Davante Adams  304.60
27 Antonio Brown  303.70
28 Mitchell Trubisky  302.77
29 James Conner  294.50
30 Michael Thomas  294.50
31 Adam Thielen  290.30
32 Eli Manning  285.21
33 Melvin Gordon  281.00
34 Mike Evans  278.40
35 Baker Mayfield  275.89
36 JuJu Smith-Schuster  271.60
37 Eric Ebron  264.70
38 James White  264.60
39 Matthew Stafford  263.93
40 Robert Woods  261.10
41 Joe Mixon  260.40
42 Derek Carr  259.84
43 David Johnson  251.40
44 Kareem Hunt  250.70
45 Case Keenum  249.44
46 Keenan Allen  245.60
47 Jameis Winston  243.48
48 Josh Allen  243.11
49 Carson Wentz  241.60
50 Stefon Diggs  239.80
51 Jared Cook  238.10
52 Phillip Lindsay  235.50
53  Brandin Cooks  233.70
54 Tarik Cohen  230.59
55 Chris Carson  229.90
56 T.Y. Hilton  228.90
57 Derrick Henry  224.31
58 Odell Beckham  220.84
59 Blake Bortles  217.55
60 Marcus Mariota  216.02
61 Tyler Lockett  213.70

62 Nick Chubb  213.40
63 Adrian Peterson  213.00
64 Tyler Boyd  210.10
65 Tevin Coleman  204.60

66 Jordan Howard  203.50

67 Lamar Jackson  201.54

68 Kenny Golladay  201.10
69 Jarvis Landry  200.87
70 Andy Dalton  199.04
71 Sam Darnold  198.35
72 Marlon Mack  198.10
73 Calvin Ridley  197.00
74 Amari Cooper  196.50
75 Julian Edelman  194.67
76 Kenyan Drake  193.70
77 Austin Hooper  192.00
78 Aaron Jones  187.90
79 Ryan Fitzpatrick  187.34
80 Emmanuel Sanders  187.17
81 Lamar Miller  184.60
82 Kyle Rudolph  183.40
83 Chris Godwin  180.70
84 Larry Fitzgerald  180.13
85 Matt Breida  178.40
86 Trey Burton  177.60
87 Mike Williams  176.70
88 Alshon Jeffery  176.50
89 Corey Davis  175.60
90 Adam Humphries  175.20
91 Austin Ekeler  174.30
92 Mohamed Sanu  174.15
93 Sterling Shepard  172.00
94 David Njoku  171.90
95 Alex Smith  170.50
96 Ryan Tannehill  169.61
97 Joe Flacco  169.00
98 Sony Michel  168.10
99 Vance McDonald  168.00
100 Rob Gronkowski  167.60

Granted, this list neglects fumbles, which may be a larger factor for some players than others, it's still a decent list to see the trends of how the points scored would have broken down by position last year. With 14 QBs in the Top 25, it's evident that a late QB strategy would have paid dividends last year. Also, with only 3 tight ends in the top 25 and a large drop in total points to the 2nd tier tight ends, it's clear Kelce, Ertz, or Kittle won a lot of SFB8 leagues last year. Furthermore, a top 5 running back was a great help as well in 2018: Elliot, CMC, Barkley, Kamara, and Gurley bested their positional peers by a good 50 points or more. As for the Wide Receivers, there were nine WR1s within 50 points of each other, with the next tier of WRs containing about twelve WR target leaders for their team. In summary, a first round running back pick, a second round WR, third round TE likely would have been a very strong start in the Scott Fish Bowl last summer. Lucking out with some combination of top 7 QBs with later round picks, that hopefully included Patrick Mahomes, would have also paid dividends. Filling in the other rounds with surprisingly productive pass catching RBs and slot WRs could have easily led to a championship run.

The Standard of Righteousness

So often, we are tempted to quantify goodness on a continuous scale where some neighbors are better than others (and where we are typically better than all our neighbors).

Suppose we rate the percentage of the time that someone lives a truly unselfish life as the variable of interest. Then a comparison of 5 people in your neighborhood might look something like this...

If God grades on a curve, we are tempted to say that person B is a "good" person, person C is "pretty good", person D and E are "Ok", and maybe person A is "selfish".

However, the trouble comes when we realize there's a perfect, cosmic judge in heaven, whose yardstick for righteousness is perfection. The only person to meet this standard was God's son sent for us, Jesus Christ. When we put the above numbers up against Jesus Christ, we get an entirely different picture...

Clearly in this light, righteousness is an all-or-nothing proposition. We all fall short and our petty attempts to be good amount to nothing in the eyes of an all-knowing, all-seeing God. The only way we are going to pass His Standard is if we get to work in a group with Jesus (where Jesus is that straight A student who does all the work and we skate by on his merit).

This is a gross simplification of the concept of the depravity of man, but hopefully a clear one. The standard is not your neighbor, but the standard is Jesus Christ. Unless you appeal to that higher power, you are not going to get into heaven on your own righteousness.

Analysis of Spatial NBA Shot Data in a Polar Coordinate System

Introduction:

With the rise of player tracking data, spatial data analysis is likely the future of sports analytics. NBA “shot-charts” may yield valuable information about not only the distance, but also the angle of the attempt. It was hypothesized that after adjusting for distance, the difficulty of a three-point shot attempt increases with the angle relative to the perpendicular bisector of the baseline. This analysis was undertaken with an eye to further optimize one of the most efficient shots in professional basketball.

Methods:

A Web-Crawling script in Python 3.4 in combination with the Beautiful Soup 4 package¹ was used to mine shot location data from the box-score of every NBA game from the last two regular seasons on www.basketball-reference.com. Pixel information was converted to feet from the cylinder and validated against the reported shot distance by basketball reference. The correlation between radial distance and reported shot distance was R=0.9994. Using the hoop as the origin, polar coordinates were used to test and quantify the strength of effect of the absolute value of shooting angle upon shot success via a distance-adjusted logistical regression model, fit to 2016/2017 season and validated in 2017/2018 data.

Results:
Exploratory analysis in the training set suggests that distance and angle of field goals are interactive in nature.  Sub-analysis was conducted in short (3-8 ft), middle (8-16 ft), and long (16-24 ft) range 2 pointers as well as 3 pointers. Short range shots were still found to be interactive with respect to angle and distance (p=0.022). Only the angle was found to affect middle range shots (odds ratio of 0.878, 95% CI 0.836-0.922) suggesting that the odds of success decrease as shooters move off the perpendicular bisector of the baseline (PBB). In longer range shots, the angle becomes insignificant (p=0.838), but additional distance can change odds of success (OR = 0.977, 95% CI = 0.963-0.991). Three point attempts were interactive in nature (p=0.002), so analysis was restricted to traditional 23’9” attempts to rule out the confounding “sweet spot” type of three pointers. Findings were similar to that of the longer jump shots, with only increased distance compounding the difficulty of the shot (OR = 0.920, 95% CI = 0.896-0.945). Similar results regarding significance were obtained in the test set.

Conclusion:
In summary, while the relationship between shot distance and angle of is a complex one, it seems that the angle of attempt doesn’t provide any analytic edge with respect to three point attempts. However, in the middle range shots (8-16 ft), the closer shots to the perpendicular bisector of the baseline tend to have a better chance of success.

References:

1.       https://www.crummy.com/software/BeautifulSoup/bs4/doc/

2.       https://www.basketball-reference.com/play-index/tgl_finder.cgi

3.       Hosmer, D. & Lemeshow, S. (2000). Applied Logistic Regression (Second Edition). New York: John Wiley & Sons, Inc.

4.       Long, J. Scott (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications.