Author: Matt

Wellington 2017 Stats

Here are my standard stats tables for the Wellington round of the HSBC Sevens World Series.

Adjusted Rates

Team Adj Try Rate Opponent Adj Try Rate Adj Gather Rate Opponent Adj Gather Rate Adj Margin
South Africa 0.60 0.05 1.00 0.89 32.01
Fiji 0.49 0.21 0.94 0.79 17.19
Canada 0.57 0.34 0.97 0.74 15.06
New Zealand 0.42 0.30 0.99 0.82 9.04
Scotland 0.45 0.29 0.94 0.89 8.68
England 0.36 0.30 0.92 0.72 7.00
Argentina 0.37 0.34 0.92 0.94 0.59
Australia 0.39 0.37 0.85 0.86 0.08
Kenya 0.36 0.41 0.87 0.91 -1.74
France 0.34 0.46 0.80 0.84 -5.76
USA 0.29 0.40 0.86 0.92 -6.45
Wales 0.31 0.39 0.80 0.92 -7.47
Samoa 0.27 0.53 0.81 0.88 -13.22
Russia 0.12 0.45 0.85 0.94 -17.76
Japan 0.23 0.54 0.84 0.97 -18.32
Papua New Guinea 0.13 0.66 0.65 0.97 -29.76

Possession

Team Games Opps Opp Rate Gathers Gather Rate
Argentina 6 48 8.00 44 0.92
Australia 6 52 8.67 45 0.87
Canada 6 42 7.00 41 0.98
England 5 43 8.60 41 0.95
Fiji 6 56 9.33 53 0.95
France 5 42 8.40 34 0.81
Japan 5 44 8.80 37 0.84
Kenya 6 55 9.17 49 0.89
New Zealand 6 50 8.33 48 0.96
Papua New Guinea 5 47 9.40 31 0.66
South Africa 6 49 8.17 48 0.98
Russia 6 48 8.00 41 0.85
Samoa 6 47 7.83 39 0.83
Scotland 6 49 8.17 43 0.88
USA 5 44 8.80 38 0.86
Wales 5 44 8.80 36 0.82

Scoring

Team Tries Try Rate Con Atmpts Cons Con Rate
Argentina 19 0.43 19 10 0.53
Australia 17 0.38 17 9 0.53
Canada 21 0.51 21 12 0.57
England 17 0.41 16 12 0.75
Fiji 22 0.42 22 17 0.77
France 11 0.32 11 11 1.00
Japan 5 0.14 4 3 0.75
Kenya 21 0.43 21 14 0.67
New Zealand 20 0.42 20 13 0.65
Papua New Guinea 5 0.16 5 4 0.80
South Africa 28 0.58 28 22 0.79
Russia 7 0.17 7 1 0.14
Samoa 14 0.36 14 11 0.79
Scotland 18 0.42 18 13 0.72
USA 14 0.37 14 9 0.64
Wales 14 0.39 14 7 0.50

Opponent Possession

Team Games Opponent Opps Opponent Opp Rate Opponent Gathers Opponent Gather Rate
Argentina 6 45 7.50 41 0.91
Australia 6 51 8.50 44 0.86
Canada 6 43 7.17 34 0.79
England 5 40 8.00 29 0.72
Fiji 6 56 9.33 48 0.86
France 5 39 7.80 34 0.87
Japan 5 43 8.60 43 1.00
Kenya 6 53 8.83 46 0.87
New Zealand 6 50 8.33 41 0.82
Papua New Guinea 5 50 10.00 48 0.96
South Africa 6 49 8.17 44 0.90
Russia 6 48 8.00 45 0.94
Samoa 6 51 8.50 42 0.82
Scotland 6 50 8.33 46 0.92
USA 5 43 8.60 39 0.91
Wales 5 43 8.60 38 0.88

Opponent Scoring

Team Opponent Tries Opponent Try Rate Opponent Con Atmpts Opponent Cons Opponent Con Rate
Argentina 12 0.29 12 9 0.75
Australia 17 0.39 17 12 0.71
Canada 14 0.41 14 10 0.71
England 9 0.31 9 6 0.67
Fiji 15 0.31 15 9 0.60
France 17 0.50 17 14 0.82
Japan 24 0.56 24 18 0.75
Kenya 15 0.33 15 6 0.40
New Zealand 12 0.29 12 8 0.67
Papua New Guinea 30 0.62 30 21 0.70
South Africa 4 0.09 4 1 0.25
Russia 20 0.44 19 11 0.58
Samoa 19 0.45 19 13 0.68
Scotland 15 0.33 14 9 0.64
USA 14 0.36 14 10 0.71
Wales 14 0.37 14 10 0.71

Passing

Team Passes Errors Err Rate Opponent Passes Opponent Errors Opponent Err Rate
Argentina 223 14.0 6.3% 190 11.0 5.8%
Australia 212 13.0 6.1% 211 15.0 7.1%
Canada 250 8.0 3.2% 156 7.0 4.5%
England 255 11.0 4.3% 172 8.0 4.7%
Fiji 192 13.0 6.8% 195 22.0 11.3%
France 160 10.0 6.2% 117 10.0 8.5%
Japan 173 13.0 7.5% 177 7.0 4.0%
Kenya 231 16.0 6.9% 209 14.0 6.7%
New Zealand 260 14.0 5.4% 197 16.0 8.1%
Papua New Guinea 145 14.0 9.7% 254 12.0 4.7%
South Africa 141 9.0 6.4% 197 14.0 7.1%
Russia 219 16.0 7.3% 249 13.0 5.2%
Samoa 157 11.0 7.0% 279 15.0 5.4%
Scotland 258 7.0 2.7% 268 15.0 5.6%
USA 199 16.0 8.0% 163 8.0 4.9%
Wales 201 16.0 8.0% 202 11.0 5.4%

Penalties and Cards

Team Penalties Against Yellow Cards Red Cards Opponent Penalties Opponent Yellow Cards Opponent Red Cards
Argentina 15 2 0 22 0 0
Australia 18 0 0 29 3 0
Canada 21 0 0 18 1 0
England 19 0 0 12 1 0
Fiji 17 3 0 15 0 0
France 14 0 0 9 1 0
Japan 17 1 0 20 1 0
Kenya 22 1 0 14 0 0
New Zealand 17 1 0 19 2 0
Papua New Guinea 9 1 0 15 2 0
South Africa 20 0 0 21 2 0
Russia 22 2 0 19 1 0
Samoa 23 6 0 17 0 0
Scotland 13 1 0 19 3 0
USA 16 1 0 17 2 0
Wales 10 0 0 11 0 0

Defensive Contact

Team Contact Opps D Successes D Failures Offloads D Success Rate
Argentina 110 63 27 20 57.3%
Australia 170 99 38 33 58.2%
Canada 95 59 20 16 62.1%
England 98 59 23 16 60.2%
Fiji 119 62 33 24 52.1%
France 85 48 22 15 56.5%
Japan 102 60 30 12 58.8%
Kenya 109 73 18 18 67.0%
New Zealand 121 73 23 25 60.3%
Papua New Guinea 152 59 60 33 38.8%
South Africa 163 109 30 24 66.9%
Russia 139 75 31 33 54.0%
Samoa 152 76 44 32 50.0%
Scotland 139 96 23 20 69.1%
USA 102 58 20 24 56.9%
Wales 107 68 18 21 63.6%

Offensive Contact

Team Contact Opps D Successes D Failures Offloads D Success Rate
Argentina 151 87 43 21 57.6%
Australia 130 81 33 16 62.3%
Canada 160 101 33 26 63.1%
England 99 54 29 16 54.5%
Fiji 131 70 25 36 53.4%
France 128 83 29 16 64.8%
Japan 112 71 13 28 63.4%
Kenya 152 82 41 29 53.9%
New Zealand 138 76 35 27 55.1%
Papua New Guinea 81 48 16 17 59.3%
South Africa 89 49 28 12 55.1%
Russia 151 112 26 13 74.2%
Samoa 132 75 41 16 56.8%
Scotland 116 63 25 28 54.3%
USA 110 51 24 35 46.4%
Wales 107 48 27 32 44.9%

Scrums

Team Scrums Wins Win % Opp Scrums Wins Win %
Argentina 3 2 66.7% 7 0 0.0%
Australia 8 8 100.0% 10 0 0.0%
Canada 7 6 85.7% 11 0 0.0%
England 5 5 100.0% 4 1 25.0%
Fiji 15 15 100.0% 9 1 11.1%
France 6 6 100.0% 7 0 0.0%
Japan 8 8 100.0% 7 0 0.0%
Kenya 9 9 100.0% 7 0 0.0%
New Zealand 12 12 100.0% 9 0 0.0%
Papua New Guinea 10 8 80.0% 6 0 0.0%
South Africa 8 8 100.0% 7 0 0.0%
Russia 12 11 91.7% 13 1 7.7%
Samoa 7 6 85.7% 6 1 16.7%
Scotland 10 9 90.0% 8 1 12.5%
USA 1 1 100.0% 9 0 0.0%
Wales 10 10 100.0% 10 2 20.0%

Lineouts

Team Lineouts Wins Win % Opp Lineouts Wins Win %
Argentina 5 5 100.0% 5 0 0.0%
Australia 12 9 75.0% 4 2 50.0%
Canada 10 10 100.0% 3 1 33.3%
England 10 10 100.0% 8 1 12.5%
Fiji 6 6 100.0% 6 0 0.0%
France 6 4 66.7% 7 2 28.6%
Japan 10 5 50.0% 8 0 0.0%
Kenya 3 3 100.0% 7 2 28.6%
New Zealand 8 8 100.0% 2 1 50.0%
Papua New Guinea 3 0 0.0% 4 0 0.0%
South Africa 5 5 100.0% 13 3 23.1%
Russia 2 2 100.0% 6 1 16.7%
Samoa 6 3 50.0% 10 4 40.0%
Scotland 5 3 60.0% 7 0 0.0%
USA 8 7 87.5% 6 1 16.7%
Wales 3 3 100.0% 6 1 16.7%

Receiving Kickoffs

Team Kickoffs Opp KO Errors Wins (w/o Errors) Win % (w/o Errors)
Argentina 18 2 13 81.2%
Australia 20 2 12 66.7%
Canada 15 0 15 100.0%
England 11 1 8 80.0%
Fiji 16 2 11 78.6%
France 21 2 12 63.2%
Japan 25 0 21 84.0%
Kenya 18 1 11 64.7%
New Zealand 15 1 12 85.7%
Papua New Guinea 29 3 14 53.8%
South Africa 10 0 9 90.0%
Russia 23 0 18 78.3%
Samoa 19 1 13 72.2%
Scotland 18 0 13 72.2%
USA 16 2 9 64.3%
Wales 16 0 9 56.2%

Kickoffs

Team Kickoffs Errors Wins Win % (w/ Errors)
Argentina 20 1 4 20.0
Australia 18 0 6 33.3
Canada 23 0 8 34.8
England 19 2 9 47.4
Fiji 24 1 7 29.2
France 11 0 4 36.4
Japan 8 0 0 0.0
Kenya 21 3 5 23.8
New Zealand 22 0 7 31.8
Papua New Guinea 9 0 2 22.2
South Africa 28 2 4 14.3
Russia 11 1 1 9.1
Samoa 20 1 7 35.0
Scotland 20 1 3 15.0
USA 16 3 3 18.8
Wales 16 1 3 18.8

Rucks

Team Rucks Wins Win % Opp Rucks Wins Win %
Argentina 74 64 86.5% 52 8 15.4%
Australia 70 63 90.0% 68 5 7.4%
Canada 90 79 87.8% 53 8 15.1%
England 44 37 84.1% 48 9 18.8%
Fiji 44 35 79.5% 48 8 16.7%
France 71 62 87.3% 41 5 12.2%
Japan 52 42 80.8% 50 5 10.0%
Kenya 64 58 90.6% 58 8 13.8%
New Zealand 59 52 88.1% 56 11 19.6%
Papua New Guinea 23 19 82.6% 44 2 4.5%
South Africa 36 33 91.7% 91 16 17.6%
Russia 97 84 86.6% 55 7 12.7%
Samoa 62 51 82.3% 54 6 11.1%
Scotland 55 47 85.5% 70 10 14.3%
USA 31 26 83.9% 51 10 19.6%
Wales 32 27 84.4% 53 6 11.3%

Breaks

Team Breaks Half Breaks Opponent Breaks Opponent Half Breaks
Argentina 12 11 14 8
Australia 12 14 21 9
Canada 22 12 13 7
England 13 10 13 8
Fiji 18 8 15 7
France 15 5 17 5
Japan 9 12 17 8
Kenya 23 11 13 6
New Zealand 14 10 16 5
Papua New Guinea 10 4 16 7
South Africa 17 2 10 7
Russia 10 10 22 16
Samoa 18 9 13 6
Scotland 23 7 16 10
USA 14 4 10 15
Wales 15 8 16 12

Wellington 2017 Pool Predictions

Using adjusted opportunity, gather, try, and conversion rates I am able simulate the score of matches. Then simulating each pool’s games 1000 times, I am able to predict each team’s chances of winning their pool or finishing in the top two. This method of simulating accommodates all of the different ways a pool can finish.

The simulations only use data from Dubai and Cape Town so I believe there is some overfitting happening. More than likely, each team should be closer to 50/50 than they are shown. Also, injuries are not accounted for. I don’t expect South Africa to be as dominant without Kyle Brown and Cecil Afrika. Lastly, I do not have data for Papua New Guinea so they are using data from Uganda. But considering PNG’s performance in previous tournaments, Uganda’s stats are only helping PNG’s chances.

Pool A

Team Pool Win % Quarter-Final %
England 75.7 % 94.5 %
Argentina 16.8 % 67.2 %
Kenya 7.5 % 37.4 %
PNG 0.0 % 0.9 %

Pool B

Team Pool Win % Quarter-Final %
South Africa 87.2 % 98.8 %
Fiji 11.2 % 68.8 %
Australia 1.6 % 32.4 %
Japan 0.0 % 0.0 %

Pool C

Team Pool Win % Quarter-Final %
USA 50.8 % 82.3 %
New Zealand 36.7 % 71.5 %
France 11.4 % 37.6 %
Samoa 1.1 % 8.6 %

Pool D

Team Pool Win % Quarter-Final %
Scotland 65.8 % 92.7 %
Wales 29.3 % 81.5 %
Canada 4.9 % 23.0 %
Russia 0.0 % 2.8 %

January 2017 Update

Over the last few weeks I’ve been posting tables of various stats from the first two rounds of the Sevens World Series. There are currently tables for:
Adjusted Possession and Scoring Rates
Raw Possession and Scoring
Passing and Passing Errors
Breaks and Half-Breaks
Penalties and Cards

I will post others for tackles, rucks, scrums, lineouts, and kickoffs. These tables will include each category’s corresponding errors. But I’m currently working through how best to address those errors. For example, when considering missed tackles, do you lump broken tackles and stepped defenders together or list them separately? Both show the ineffectiveness of a defense but they obviously aren’t identical. What about offloads? Some coaches consider these as missed tackles. Do I lump those in or break them out?

At some point there are too many numbers to make sense of on screen. Consolidation is necessary not just for applicability but readability. To that end I plan to update the tables to allow sorting as well as provide a better mobile experience. Since I don’t have fancy acronyms for the column headers, the long words are squeezed and often break across multiple lines when viewed on small screens. I think the current tables get the point across but need eventual improvement.

These are just a few of the things I’m spending my time on behind the scenes here. As always, if you have questions or suggestions, let me know.

Rio 2016 Review: Conversions and Centering

Injera for the win... © World Rugby
Injera for the win… © World Rugby

This is the first installment of an Olympic review series, in which I will analyze team performance in different facets of the game. This particular post examines conversion kicking and how well teams centered their tries. I feel these skills are often overlooked or at least not well measured. To better measure these skills I will attempt to isolate them from each other and, when evaluating conversion kicking, do so relative to the difficulty of the kick. Continue reading “Rio 2016 Review: Conversions and Centering”

Terms for Evaluating Possession

Gillies Kaka and Tom Bowen race for possession © World Rugby
Gillies Kaka and Tom Bowen race for possession © World Rugby

This post will outline some of the main statistics you’ll see on this site. In particular, I’m attempting to provide clarity with regards to possessing the ball. In my opinion, rugby possession isn’t as clearly defined as in many other sports and the possession sources vary greatly. These different sources yield different expectations of scoring and, as such, should be evaluated differently. Some statistics that I use require detailed explanation. Some of these stats I devised myself and they are still developing as I think about and apply them.
Continue reading “Terms for Evaluating Possession”

Argentina 17, USA 14 – Rio 2016

ArgLineup

USALineup

Don’t forget the referee, Craig Joubert.

Why this game?
The opening Olympic match for both the USA and Argentina. 

Narrative
Argentina and the USA finished fifth and sixth in the 2015-2016 World Series, only separated by two points. They split their two matches in the season 1-1. Considering both teams are likely to beat Brazil and lose to Fiji, this match is crucial to advancing to the Olympic quarter-finals.

Continue reading “Argentina 17, USA 14 – Rio 2016”

USA 26, Fiji 19 – London 2016

FijiUSALondon2016Lineups

Why this game?
This was the USA’s lone win against Fiji during the 2015-2016 circuit as Fiji went 4-1 with wins by 10, 9, 7 and 5. Pool mates in Rio, I’m curious what to expect when they match up on August 10th.

Narrative
As the third-place match in London, this was the last game of the season for both teams. The Americans dropped a disappointing semi-final to Scotland after thrashing New Zealand in the quarters. Fiji clinched the series in their quarter-final win over France before losing to South Africa by 5 in their semi-final. Fiji seemingly had little motivation for the game and confirmed this by turning over the first three kickoffs.

Continue reading “USA 26, Fiji 19 – London 2016”

Reconsidering Sevens Assumptions

Motivation

My desire to gather more descriptive rugby sevens statistics began when I was coaching the University of Michigan. We had been invited to participate in the Collegiate Rugby Championship in June 2014 and had about six months to educate ourselves on sevens after our fall fifteens season. We’d played a few tournaments but had never dedicated significant time to sevens. Although I had strategies and techniques from my time as a player I saw a greater variety in other teams. I was never satisfied blindly allocating team practice time without a decent answer to the question, “Why?”. So I started trying to answer that for sevens.

I’ve spent a fair amount of time playing with advanced sport statistics, mostly in baseball and college football. The dearth of rugby statistics, fifteens and sevens, to this day still surprises. I understand they are both complex, fluid games but there is next to nothing available. Most statistics are taken by individual professional teams with few accepted industry standards. Their statistics are not public for vetting by interested fans. The Aviva Premiership offers some team statistics for their matches but provides little league-wide context. If a team had 212 passes in a game is that a lot or not a lot? Does it matter? If they rucked at 96% for the match should that be celebrated or looked at as a point of improvement? Super Rugby doesn’t even offer these statistics. Where is the baseball-reference.com of rugby?
Continue reading “Reconsidering Sevens Assumptions”