US Open

/ #r-markdown #footgolf 

Introduction

The American FootGolf League (AFGL) and River Ridge Golf Course hosted the 2018 U.S. Open in Oxnard, CA on April 26th - 29th. The tournament saw players attend from all over the world for the first major of the FIFG World Tour. The course was very difficult as it was both long (~3600 yds), sloped, fast, and occassionally windy.

I played in the tournament myself, albeit not well, and anecdotally found that certain holes were more troublesome than others, especially depending on time of day (ex, windy). As is the case in all tournaments there is always commiseration after the round and talk about the holes that ruined it all. I thought to myself, I wonder if the scores would reflect the sentiment people had of certain holes? Did a majority also suffer on Hole X? Did the time of day affect that?

This little analysis seeks to help shed some light on that as well as other fun statistics about the event. Hope you enjoy.

Data

All the charts you see here are created from a limited dataset. The dataset consists of the top 100 men’s players. This is for few reasons: 1) It’s difficult and takes time to scrape, clean, and format the data 2) 100 players is a reasonable enough sample size 3) I needed a large enough sample size of players kicking from the same tees in case I wanted to do significance testing. Unfortunately Senior’s or Women’s division couldn’t provide that. I’m optimistic that will change in the future.

Summary Stats

hole par avg.score better eagles birdies pars bogeys doubles worse par.avg
1 4 5.363 0 0 1 57 143 62 37 1.363
2 4 4.407 0 2 27 144 104 20 3 0.407
3 4 3.080 0 69 164 48 14 3 2 -0.920
4 5 5.900 0 1 6 91 148 39 15 0.900
5 3 3.140 0 0 26 213 54 7 0 0.140
6 5 5.423 0 1 44 113 113 27 2 0.423
7 4 4.757 0 0 9 117 123 43 8 0.757
8 3 3.793 0 0 5 87 175 31 2 0.793
9 4 5.110 0 0 4 66 149 61 20 1.110
10 4 4.650 0 1 11 131 120 25 12 0.650
11 5 4.927 0 8 92 131 55 12 2 -0.073
12 3 3.340 0 1 19 185 70 22 3 0.340
13 4 3.863 0 3 88 169 31 5 4 -0.137
14 5 4.617 0 10 132 122 35 1 0 -0.383
15 4 4.337 0 4 40 140 90 20 6 0.337
16 4 4.020 0 1 78 154 52 11 4 0.020
17 4 4.203 0 0 35 179 76 10 0 0.203
18 3 2.920 0 2 55 212 28 2 1 -0.080

How were the scores distributed per round?

Mean scores per round were near identical with day 2 showing slightly, but not significant, improved results. Day 3 was the worst.
round mean.score
1 0.325

How were the scores distributed per hole?

Holes 1 and 9 show the highest mean scores. The series of 7,8,9 was quite a gauntlet on it’s own as well. Hole 3 was the easiest.

hole mean.score
1 1.3633333
2 0.4066667
3 -0.9200000
4 0.9000000
5 0.1400000
6 0.4233333
7 0.7566667
8 0.7933333
9 1.1100000
10 0.6500000
11 -0.0733333
12 0.3400000
13 -0.1366667
14 -0.3833333
15 0.3366667
16 0.0200000
17 0.2033333
18 -0.0800000

How were the scores distributed per hole per round?

The holes played pretty consistently over the three rounds. Hole 1 on round 1 and round 3 seems to have caused a lot of trouble.

hole round mean.score
1 1 1.3633333
2 1 0.4066667
3 1 -0.9200000
4 1 0.9000000
5 1 0.1400000
6 1 0.4233333
7 1 0.7566667
8 1 0.7933333
9 1 1.1100000
10 1 0.6500000
11 1 -0.0733333
12 1 0.3400000
13 1 -0.1366667
14 1 -0.3833333
15 1 0.3366667
16 1 0.0200000
17 1 0.2033333
18 1 -0.0800000

Did time of day have an impact on scoring?

Although I haven’t run any significance testing on it, it seems that the time of day one played the hole did have an impact on certain holes. On Hole 1 for example, we can see that the mean score went from near 1 over par in the morning to 2 over par by 1pm. Other holes had the opposite results. Hole 11 seems to have gotten better later in the day.

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