When Data Analytics meet NRL

Analysing the effects of lateral movements on speed and performance

EdgeRed participated in the 2019 NRL DataJam and took home 2nd place; competing against teams from major companies across Australia. Despite our initial limited knowledge on rugby league, we analysed the effect of lateral movement and brought a fresh perspective into the competition. Read about our analysis featured on ABC News.

How do we measure lateral movement?

As part of our analysis we had access to two key data sets:

1. Transaction data which recorded every event of the game (e.g. when a tackle is made, when there is a try, which players were tackled etc.)

2. Tracking data (GPS data) which tracked every player’s position every tenth of a second

To measure lateral movement, we use the data provided and developed the below three metrics:

How do different positions run?

Based on the data provided, we found that an average player runs about 5.5km per game. For every metre forward, they will cover about 0.7 metres horizontally, which means that over the course of a game, an average player usually runs in a 35 degree angle.

Analysing this by specific player positions, we can see that fullbacks run the most laterally (highest red dot) and have a lateral ratio of 1; meaning for every metre they covers vertically down the field, they are also covering a metre horizontally across the field (i.e. a 45 degree angle). This is not surprising given the nature of their role to be at the furthest back in defense.

Do better teams run more laterally? To answer this, we compared different teams’ lateral ratio at each player position and their respective ladder record for the 2019 season. Very interestingly, we were able to present a clear correlation whereby teams which were more successful in the 2019 season had positions with a much greater lateral ratios. Unfortunately due to our NDAs, we are unable to share these findings with you in this blog post.

How does lateral movement relate to trys?

If an offensive team moves more laterally, does it improve their ability to score a try? In the chart below, we represent each set with a bubble, with the defensive team lateral distance on the x-axis against the offensive team lateral distance on the y-axis.

Green area: Offensive team moved more laterally

Yellow area: Defensive team moved more laterally

Red bubble: Tried

Grey bubble: No try

Our analysis showed that in sets where the Defense moved more (yellow area), the offense was only able to score in 10% of the time. On the flip side, where offense moved more (green area), they were able to score on 30% of these situations. That’s a 3x increase in the probability if the offensive team moves more laterally than their opponent.

We found that an offensive team with more lateral movement is 3x more likely to score a try.

Final thoughts

The analysis we showed proved that there is a strong correlation between teams exhibiting higher lateral movements (relative to their competition), and their success in a game. Whilst you can say that this insight in itself is not very surprising or groundbreaking, it opens up a new line of thinking within sporting analysis. The ability to measure lateral movement can be used to assess certain plays. Coaches can monitor this in their training and recruitment processes to track improvement over time. Drawing parallel to other sports, such as cricket and NBA (e.g. "Moreyball"), we are seeing more and more use of data analytics to gain a competitive advantage over other teams.

As newly converted fans, we are excited for the 2020 NRL season kicking off this weekend!

We had the honour of presenting our findings to the Sydney Roosters at their head office. Thank you to Precision Sourcing and KPMG for hosting this innovative event!

About Us

EdgeRed is a boutique data and analytics consultancy specialising in delivering high quality outcomes for our clients. Drop us a note and we'll be happy to have a chat regarding your data and analytics needs.