NRL Round 11 advanced statistics – Involvement Rate

Round 11 is complete and it’s time to look at another advanced statistic, this time Involvement Rate. If you’re new to the site, there’s a primer on Involvement Rate in the articles section which will explain this statistic in more detail.

Here’s the top 20 chart for Involvement rate for all players for Round 11

Rhys Kennedy also locks up the all minutes Involvement Rate chart, purely from his limited minute performance against the Storm, involved in nearly 30% of all plays whilst on the field. Jai Whitbread from the Titans was the only other player above 25% for the round.

Here’s the top 20 for Involvement Rate from players who played at least 40 minutes

Canberra’s Ryan Sutton put in the most work of all players who played at least 40 minutes this round, being involved in over 23% of plays whilst on the field. Josh McGuire placed second with 22%, with Christian Welch making his regular appearance at the top of this chart at a touch under 22%.

With site favourite Daniel Alvaro joining the Warriors on loan and being named on the bench it’s quite possible we’ll see Alvaro join his regular nemesis Welch at the top of this list for the next month.

Finally for the season, and we have a tie currently between two Titans – rookie Jaimin Jolliffe and Jarrod Wallace both sitting at an involvement rate of 22.9%. Their lead is not substantial enough that they can stave off the pack – any player currently over 21% has a solid chance of picking up the top spot this season.

Nat Butcher and Lindsay Collins continue to take on a lot of the workload for the Roosters, as they continue to miss a number of key forwards. Collins in particular has been extremely impressive in limited or extended minutes, and has shaped as one of the most improved players this season.

NRL Round 11 advanced statistics – Tackle %

With NRL Round 11 in the books we can update another of the advanced statistics I used to trackl player performance during the season, Tackle %. If you’re new to the site, there’s a primer in the articles section that goes into more detail on Tackle %.

Here’s the Tackle % top 20 chart for all minutes after Round 11.

Brisbane’s Rhys Kennedy topped the chart this week with a 47% tackle %, making 12 tackles in just 12 minutes against the Storm. Lindsay Collins continued his strong season with a Tackle % of 43% in just 26 minutes against the Warriors, with his team mate Poasa Faamausili not far beind at a shade under 43%.

Let’s move on to the 40 minute plus Tackle % chart

Another ROoster takes top spot for those players who spent half a game on the field, with Jake Friend sporting a Tackle % of nearly 37%, making 45 tackles in 60 minutes.

Cameron McInnes placed third with a Tackle % of 32.62%, and was one of only two 80 minute players on this list this week, the other being Jake Trbojevic who placed 19th with a Tackle % of 27.65%.

Tom Starling continues to put in a lot of work replacing Josh Hodgson, with a Tackle % of 31% in 52 minutes.

Finally here is the 2020 season chart for Tackle %

Jarrod Wallace still hangs on to first place for the season, with a tackle % of 36.73%, but he has dropped over 1% in the last week. Nat Butcher may end up catching him at this rate, currently only trailing him by 0.6%

As noted last week, rookie Jaimin Jolliffe is holding on to third spot and not slowing down, unlike Toby Rudolf who is dropping down the list with an increased role. This is unsurprising, as most interchange players generally see a drop off in effort rate as their minutes increase.

NRL Round 11 advanced statistics – Run %

Round 11 is complete, meaning we can now look back at the advanced statistics I use to track players through the season. The first up is Run %, and if you’re new to the site you can find a primer here.

Here’s the Top 20 all minutes chart for Round 11

Royce Hunt absolutely smashed the rest of the competition, not only sporting a 30.5% Run %, which is unheard of from players who spend more than 10 minutes on the field, but he amassed 20 runs in 28 minutes. If you scan down the list, the only other player who put in a similar volume of Runs was Toby Rudolf, who had 5 more runs than Hunt but played another 30 minutes to do so.

Now we move on to the 40 miunte plus chart for Run %.

Dragons prop Blake Lawrie takes top spot with a Run % of 20%, meaning he completed a run on one of every five plays the Dragons had whilst he was on the field. Rudolf wasn’t far behind at 18.13%, with Martin Taupau the only other player above 17% this round.

Again you can see just how much work Hunt put in when there’s only four players who totalled 20 runs in this chart and all of them played significantly more mintues than him.

Finally the overall season chart for Run %.

As mentioned last week, this had become a two horse race between Andrew Fifita and Nelson Asofa-Solomona. Unfortunately with Fifita’s injury hes likely to drop out of the chart due to missing the minute restriction (>=20 minutes per game). The gap between third placed Francis Molo is large enough currently that I don’t expect Asofa-Solomona to be run down without injury or a serious change in role.

Usually later in the season I’ll combine a minute restriction with a minimum number of games played. In the past I’ve played with numbers between 200-300 minutes played as a minimum. Given the drop in games this season I might end up capping it at 200 minutes and/or five games played to see if that looks right starting next week. It will probably be a work in progress, much like the rules this season.

Are performance issues under short turn around times a myth? NRL Round 11 2020 stats and trends

There was some talk recently on NRL twitter about short turn around times after Ricky Stuart mentioned about having only five days between games. The question raised was if there was any data to back up claims that they have a negative effect on performance. Turns out there was quite a bit.

NRL Fanalytics has previously posted a fantastic chart that showed the win rate for teams with various days of rest from 1998-2019.

Their conclusion was “The longer the rest, the lesser the home ground advantage”. The chart also shows that there is practically no difference in win percentage on a five-day turnaround (50.5%) compared to seven days (50.0%).

Andrew Ferguson from the best rugby league site on the internet chimed in as well, posting a fantastic chart on twitter showing win percentage by turnaround time from 1998-2020.

It’s worth opening the image to see the win percentage by each team for each day turnaround. Yet another piece of data showing that there’s very little difference in 5-8-day turnarounds for clubs.

The evidence that five day turnarounds don’t impact winning, which corresponds with an article from the Sydney Morning Herald in 2016 which came to a similar conclusion.

NRL Physio has also posted some data on turnaround times which shows there is no substantial difference in injury rates when teams back up after 5 days.

The lack of a difference in in injury rate was also noted in an acadmeic paper by Nick Murray, Tim Gabbett and Karim Chamari in 2014, titled “Effect of Different Between-Match Recovery Times on the Activity Profiles and Injury Rates of National Rugby League Players”.

You’ll notice from the above NRL Physio Twitter conversation that I’ve also done some previous analysis for NRL Supercoach scores which also shows a very minor change in player scoring with extra rest.

It should be noted there is a danger in using fantasy points scoring as a proxy for performance because the points are awarded to make scoring within the game interesting and not necessarily as reflection of player worth. Good fantasy sport players often do not make good players in real life – there’s a reason the term “empty stats” gets thrown about regularly.

For example, one of the statistics that correlates the best with winning games, run metres, isn’t a scoring category in Supercoach (unlike NRL Fantasy). However, each runs over 8 metres are worth two points as opposed to runs under 7 metres being worth one point.

Still, it corroborates the other evidence that turn around time has little effect on performance from a players perspective. That gives us three data points (win percentage, injury rate and player performance) that is virtually unchanged with less rest.

This got me thinking, is there any other underlying evidence that short turnaround times affect performance? Maybe there’s a change in how games are played under these conditions compared to the standard rest time?

To look at this, I’ve put together the data from 2014-2020 (up to Round 11), removing Round 1, as well as the first game back from the season resumption in 2020 since they have no turnaround time. Games on 10-17 days rest have also been excluded due to a smaller sample size.

Here’s what the average NRL game looks like from a number of statistics based on turnaround times from 5 days up to 9. The sample size is what we in the industry would call “robust” if we were pitching some data to a sales and marketing team. The term “games” is probably a misnomer, as it is the instances of a team playing on each turnaround time, since the team playing on five days is usually facing a team that has had a longer recovery.      

 

Looking at the averages, teams playing under a five-day turnaround average slightly less “stuff” than six and seven days but very similar to eight days rest, and more than nine days. For example, there are around 11 fewer runs per game from teams on a short turnaround, which results in three fewer completed sets, with six fewer passes, which leads to 1.5 fewer kicks per game. These are all intertwined so the very small drops across the board make sense.

Despite doing less “stuff”, teams on a short turnaround seem to play a slightly safer style of game. They have a slightly higher completion rate, fewer offloads, higher metres per run, miss fewer tackles (as a percentage of made tackles), and concede fewer penalties.

Now that we’ve looked at the raw numbers, lets now look at the same statistics with the percentage difference between each days rest. Each column below shows the percentage difference from 5 days to 6 days, then from 6 to 7, and so forth.

Again, we can see there’s minimal changes across the board. Most of the increases are a few percentages at most, with the highest being in the 6-8% range from five to six days, and then declining by a few percentage points with each extra day to recover.

Looking for the biggest changes, there’s only three statistics with a double figure percentage.  For each of them, there’s an easy explanation.

Long kicks and attacking kicks increasing by 30+% makes sense when you pair it with the difference in runs. As mentioned above, there’s an 11 run per game difference when moving from 5 to 6 days, which is about 2% of total runs and about three sets of six if you take the 3.6 tackles per set.

If you’re adding three sets of six, those sets need to end somehow and the most likely ending is a kick. And with only four kicks per game, even adding one more would be a 25% increase.

Missed tackles increasing by 12.5% looks like a decent increase, although tackles increased by 8.5%. This indicates that teams with longer rest miss more tackles than those on five-day rest. We’re talking fractions of a percent here, from 92.3% to 92.0%, but it may again point to a safer game plan or more emphasis on defense with a short turnaround.

The takeaway from this, other than there being no substantial statistical difference in performance when looking at the break between games, is that those on a five-day turnaround may be implementing a slightly more conservative game plan. This makes sense as you have less time to game plan and focus more on recovery.       

Whilst all available evidence points to short turnarounds having minimal effect on games, this is not to say that there is no impact. Smarter people than I with access to better information will know for sure, and it’s highly likely that some players aren’t hitting their maximum speed or tracking the same distances in these games. There is also the mental side of backing up, which could influence decision making which would not necessarily be show statistically.

So, to answer the original question, how do games change with shorter turnarounds? To put it bluntly, at appears they don’t. They don’t result in a lower win %, injury rate isn’t changed, player performance doesn’t vary significantly and teams laregly output the same amount of “stuff” as they would with any other amount of rest.

This is more for the broader rugby league community who tend to blindly repeat the short turnaround myth despite an overwhelming amount of evidence that fails to support it.

If anything, the bigger story is that teams with a nine-day turnaround have slightly lower statistical averages across the board than teams playing on 5-8 days rest. But no coach is going to complain about having too much time off, are they?

Penalty and set restart update

We’re now approaching a point where there’s little need for additional penalty and set restart analysis, things are quite stable week to week. This week we had a penalty or set restart every 18 tackles, up from 16.5 last week. Here’s the breakdown of penalties and set restarts by round and you can clearly see we’ve fallen into a stable pattern.

Looking at referee averages and it’s a similar story with little change. The only difference is Adam Gee dropping his spot as the top caller of set restarts to Chris Butler, although Butler has only officiated two games in Rounds 8 and 9.

Speaking of Gee, in some big news, he broke his streak of six straight games with at least seven set restarts called in the first half, calling just seven in the whole game during the Panthers/Titans matchup. Who says referees aren’t consistent?

Net Points Responsible For

A few weeks back I went through some data on Points Responsible For as a way of highlighting playmakers who don’t always get the credit for a try through the awarding of a try assist. Scott Drinkwater was the notable example there, who had set up a number of Cowboys tries but did not receive a try assist for them.

This week, I’m looking at Points Responsible For (PRF) again, but this time including the Try Cause statistic that Fox Sports keeps showing the value of playmakers on both sides of the field.

Net Points Responsible For (NPRF) follows the same formula as before – tries *4 + try assists *4 + try contributions *4 + field goals but adds in a negative four points for every try caused.

Here’s the top 20 after Round 11.

The difference can be seen with Drinkwater in this chart, who has 8 try causes for the season. Under the normal PRF calculation, he would be ranked sixth at 7.56 PRF per game. Once you add in the defensive side, he drops to 15th with an average of just 4.00 NPRF per game. Benji Marshall is another who suffers, moving from fourth in PRF at 8.14 per game to ninth at 4.71 per game.

By far the biggest loser though is Matt Dufty who drops from fifth under PRF to 35th on the back of having 12 try causes attributed to him. His average plummets from 8.00 to 2.67 per game.

The other advantage of using NPRF is that you can also look at players who are a net negative, giving away more points than they score or contribute to. Here’s the top 20 after Round 11.

Unsurprisingly there’s a number of Broncos and Cowboys topping the list, led by Jesse Arthurs who let in 8 tries in four games, with only a try assist on the positive side. Esan Marsters with 14 try causes is also having a difficult season, with a negative NPRF of 4.4 per game thanks to having 14 opponent tries attributed to his defense, or lack of it. .  

Blake Ferguson showing up on this list is more an indication of how little he is seeing of the ball this season than any defensive deficiencies.

NRL Round 10 advanced statistics – Involvement Rate

Before the charts this week, I wanted to clear up the “Total  Plays” column based on some Twitter feedback. The “Total Plays” column is the total plays for a particular game, and not the minute adjusted number of plays a player was on the field for. The adjustment for minutes played is made in the Involvement Rate % calculation.

I’ve left the “Total Plays” unadjusted get an idea of how many opportunities to run the ball or complete a tackle. Generally, NRL games have around 300 play the balls. If you’re seeing a team with more than that number of plays, then they’re either dominating possession or playing in a low scoring game where the ball is in play more often.

If you’re new to the site and want to learn more about Involvement Rate, you can check out this post in the articles section which goes into it in more detail.

Here’s the NRL Round 10 all minutes chart for Involvement Rate

As with most weeks, it is the lower minute interchange players clogging the top of this chart, with South Sydney’s Patrick Mago again leading the way with an involvement rate of 28.6%, meaning he either made a tackle or completed a run in nearly 3 out of every 10 plays whilst on the field. Mago was number one last week with nearly 29% and is clearly making an impact in his short minutes on the field for Wayne Bennett.

Moving to the 40+ minute chart, and Lindsay Collins has relished the increased minutes for the Roosters this year and is filling the void left by Victory Radley and Sam Verrils. He was involved in 38 plays on the weekend in 41 minutes, for an Involvement Rate of 24.07%. With Angus Crichton out a number of weeks with a knee injury it is likely that there will be even more work for Collins to pick up.

Jai Arrow placed second this round with an Involvement Rate of 23.4%, getting back to his old work rate after a few rounds of limited involvement.

Finally, for the season so far, Jaimin Jolliffe leads the way with an Involvement Rate of 23.07%, slightly ahead of teammate Jarrod Wallace at 22.92%. Wallace has been dropping down the Tackle % and Involvement Rate chart with a change in his role and reduced minutes. Nat Butcher rounds out the top 3 with an Involvement Rate of 22.73%. Similar to Run % this is most likely going to end up as a two-horse race between Joliffe and Butcher.

Don’t sleep on Christian Welch either, who is surging lately and has moved up to sixth for the season with an Involvement Rate of 21.57%.