Category: Articles

The Eye Test’s Most Adequate of 2020 – The rule change affecting time in play that’s rarely talked about

This article was originally posted as part of NRL Round 14 notes and trends, August 18, 2020.

One of the things I’ve noticed over the past few rounds is that the average time of ball in play has dropped slightly to the pre Rugby League 2.0 levels. This comes after a decent increase earlier in the season once the rules were changed. Focusing just on time in possession, the last NRL three rounds haven’t had more than 57 minutes of ball in play, the three lowest rounds this season and both before V’Landysball was introduced in Round 3.

This led me to investigating why, and I put together the below chart plotting time in possession (sourced from NRL.com) against points scored per game. The blue line represents average time in possession for the first 14 rounds of the 2019 and 2020 seasons, and the yellow bars represent the average points scored per game in each round (by both teams).  There’s a reference line on these bar charts as well to show the average for 2019 and 2020. For points its about the same – 38.8 in 2019 and 39.9 in 2020.

Initially I thought that the amount of points scored was reducing the time in possession, with more tries and conversion increasing the amount of time the ball was doing nothing. But if you look at the above chart, it’s not really apparent – Rounds 8 and 11 had average game scores below 40 points, but time in possession above 62 minutes, significantly higher than other rounds this season.

I should note at this point I’ve filtered out any golden point games to normalise minutes per game. A great example of why is Round 3 2020, where the average goes from 58.81 to 61.46 if you include the Panthers v Knights drawn match which had a whopping 80 minutes of time in possession. Another note is that Round 12 2019 had only four games played due to State of Origin, which is why it looks like an outlier.

It’s not due to tries either, see below for the chart that shows why. Round 7 2020 had 8.3 tries and nearly 58 minutes of time in possession, while Round 9 this year had 6.7 tries but 62.7 minutes of possession. Again, this makes sense with the previous chart as points are a factor of tries scored.

My next thought was maybe there are fewer penalty goals? There are fewer penalties being called, so it makes sense that there were fewer penalty goal attempts this season. Whether or not that’s a good or bad thing is another discussion, especially in those instances where a team is down 2 inside the opponents 20m zone and gets a set restart. But that’s another matter for another time. Below is penalties awarded plotted against time in possession.

This led me to look at penalty goal attempts against time in possession. The data checks out – 1.6 attempted penalty goals last year against 1.1 in 2020. And they’re being taken at a lower rate too. In 2019 penalty goal attempts comprised nearly 20% of all shots at goal. In 2020, that number has dropped to just 13.6%. So that’s the likely reason for the increase in time in possession, right? Less time standing around waiting for a kick at goal.

Hang on, let’s look at something a bit closer on that chart. Round 1 and 2 had time in possession of 58 and a half minutes and an even 57 minutes, respectively. That’s more time in possession than the last three rounds under one referee and with set restarts. There’s actually been six rounds since Round with less time in possession than Round 1.

Yet Round 1 and 2 had over 2 penalty attempts per game, far higher than the rest of 2020 and more than most rounds last year to the same point. How did those two rounds still have high time of ball in play yet more penalty goal attempts?

Maybe the time elapsed during a penalty goals is counted as time in possession? If that were the case, that wouldn’t explain Round 12 having 56.6 minutes in play with 1.4 penalty goal attempts per game, while Round 8 had almost 63 minutes in play with just 0.6 penalty goal attempts per game.

Maybe the game is just faster? In this “faster pace” era, everything is up, and more stuff is being done. So far this season we’ve seen an increase in time that the ball is in play. There’s an increase in runs and play the balls as well. Although not an increase in play the ball speed.

But we do know from the first chart that the ball is in play more this season by about 8% compared to 2019 for the first 14 rounds. Runs are up nearly 10% compared to the same point in 2019. Passes are up 5%, line breaks are up 7% and tries are up 10% Everything is up! More stuff is good!

Kicks are also up 7.5% year on year, with long kicks up 17% and attacking kicks are up 7%. More stuff! But hang on – weighted kicks are down 20%. That’s strange. Forced dropouts are down 2.1%. Kicks dead are down 3%. Why would those kicks be down year on year if everything else, including other types of kicks, has increased?

The fact there’s not a corresponding increase in weighted kicks, kicks dead and dropouts, and a higher increase in attacking kicks than other statistics indicates something has changed. You might be slowly getting at where I’m leading with this and why its taken over 700 words.

To save you anymore of this shaggy dog story, here’s my crackpot theory – teams have gotten more efficient and accurate at aiming their attacking kicks just outside the goal area to avoid a seven tackle set. The rule change which came into effect in Round 1 that gives airborne attacking players the same level of protection as airborne defensive players is surely a driver for this, as Daniel Tupou was showing before succumbing to injury.

This explains the drop in weighted kicks but the large increase in attacking kicks. Fewer kicks reaching the in-goal area leads to fewer dropouts which can take up to 45 seconds each. By aiming them a bit shorter than the try line, at worst a team will give up possession less than 10 metres out or a scrum at the same point. This is a much better result than a seven-tackle set from the 20-metre line.

Why does this make such a difference in time in possession? A drop out usually takes 40-45 seconds off the clock, because the NRL has a rule saying you can take that long (another rule change with unintended consequences). In the first two rounds this season, there were 20 fewer forced dropouts than the first two rounds last season than in 2019. If you are generous and say each one takes 40 seconds, there’s 920 seconds saved across two games. Divide by 60 to get minutes and then divide again by the eight games per round and you get an extra 57 seconds saved on average per round purely from fewer dropouts.

This would account for some of the time in play change for Rounds 1 and 2 this year compared with last year. It also explains why Round 3 had only a slightly higher time in possession than Rounds 1 and 2 – the time savings from reduced penalties was cancelled out by having over five dropouts per game that round. The chart for average dropouts against time in possession is below.

These first two rounds this season serve as my exhibit A, albeit with a small sample size. There is similar average time in possession to post Round 3 (excluding the golden point draw), but there were still two referees and no set restarts. A comparable number of penalties were awarded as previous seasons yet more penalty goals attempted. The key is fewer dropouts in Rounds 1 and 2 compared to 2019, and below the average for 2020.

Need more proof that a reduction in forced dropouts might be part of the increase in the time of possession? Exhibit B – the last three rounds have had the three lowest time in possession averages this season, all under 57 minutes as noted in the first paragraph. In the last two of those rounds (13 and 14, factoring out Round 12 due to fewer games), dropouts are up 31% year on year and weighted kicks up 11%. As opposed to down 2% and 20% for the season so far. Goal attempts were down 3% over these rounds too, ruling that out as a cause as well. Why the change in kicking? Teams may be finding that their tactic of launching more bombs aimed outside the try line hasn’t been as successful and are adapting. Whatever the cause, there’s another link between time in possession and dropouts taken.

I’m not denying that there is an increase in time in possession due to the Round 3 rule changes, the reduction in penalties also plays a part. There’s an average of three fewer penalties per game this season, and with the NRL has claimed there were five penalties per game in the play the ball last season and each one costs about 22 seconds of play. If you multiple those 3 fewer penalties by 22 seconds, there’s another minute with the ball in play. Add in fewer penalty goals and there’s a bit more time gained. Yet there’s also a similar component of time being saved from fewer line dropouts.

The increase in time on possession hasn’t isn’t just a result of rule changes, but a larger and more complicated combination of change in playing style to suit for these rule changes. The consistent attribution of faster “pace” and more “stuff” being done given solely to set restarts and one referee is proving to be a false equivalence, but one that will get a lot more airtime to boost agendas. If you really wanted to speed up the game, you’d halve the clock on dropouts.

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The Eye Test’s Most Adequate of 2020 – The statistical improvements that back Todd Payten’s Cowboys appointment

This article was originally posted as part of NRL Round 17 notes and trends, September 8, 2020.

The North Queensland Cowboys announced interim Warriors coach Todd Payten as their coach for 2021 on Friday, and using the Eye Testtm it’s easy to see why. The Warriors have improved on the field under his watch and are showing a lot more enthusiasm and commitment, even after a coach they supported was removed. Their fightback against the Eels showed a desire that Warriors sides haven’t shown late in a game for quite some time.

This led me to have a look at what changed under Payten and how the Warriors improved under his tenure by looking at the teams per game statistical averages under Kearney compared with Payten’s performances.

First up I’m going to qualify everything below with a small sample size disclaimer – we’ve got six games for Kearney and eleven for Payten to analyse. These aren’t representative, more they are indicative of their performances, but within those games there are hundreds if instances of runs and tackles which gives me some comfort. It’s not like I’m writing an article purely on the win/loss percentages of teams where one win or loss would throw out my premise completely. But I digress.

Below is a chart of the percentage change for a number of statistical categories for the Warriors in 2020, with te blue dots represent the percentage difference between Kearney and Payten, and the orange dots represent the difference between Payten and Kearney. The further the circle is to the right or left, the larger the difference. Whichever colour sits on the right-hand side shows which coach had an advantage in that area, and at first glance you can see by the number of organe points on the right hand side that Payten has outperformed Kearney in the majority of these statistics.

I wanted to note that I’ve chosen percentage change because on a per game basis it’s hard to get a scale that fits average total run metres in the thousands (1400m+) with average metres per run in single digits (8.5-9.0). Otherwise it would be impossible to see some of the changes. Another reason, which if you’re becoming familiar with my posts you’ll know, is that the exact number isn’t as important to me as the size or direction of the trend. I’m looking for how much things have changed under Payten.

WIth that out of the way, let’s delve in a bit deeper to the differences and start with the first line for points scored per game – the orange dot on the right shows Payten had an increase of nearly 50% in points scored per game, with the Warriors going from 12.2 under Kearney to 18.4 per game under Payten’s stewardship. Looking at from the other perspective, the Warriors scored 34% fewer points when coached by Kearney in 2020.

Tries and line breaks were also up significantly under Payten, whilst a possession statistics like play the balls and total sets were down between 1-4%. From this group of stats, you can see that not only were Payten’s Warriors scoring more, they were doing so with less possession. That is countered by the fact they had slightly better field position, as play the balls inside the opponents half and opponents 20 metre area were up 2% and 1% respectively.

The next set of stats I wanted to focus on were runs, run metres and passing. Both teams averaged the sane number of runs (168 per game), which makes a fantastic baseline to use.

There was no change there from a quantity point of view but it’s very clear they’ve changed how they were running the ball, and its effectiveness. The first is that dummy half runs were up 44%, from 7.5 to 10.8 per game. One pass runs, your standard hit up, were down 13% under Payten, whilst general play passes were up 17%. The increase in passing wasn’t just mindlessly throwing the ball around either, as offloads increased dramatically after the coaching change, with nearly 70% more total offloads (6.7 to 11.6) and a triple figure percentage increase in effective offloads.

This would suggest that he has given his dummy halves more freedom, allowing them to skip out of the ruck and engage the line before spreading the ball wide, and the increase in passing stats shows they were playing a more expansive game compared to the safe conservative style under Kearney. He’s also unlocked their ability to promote the ball with offloads as well. Given these changes it is not surprising that they may have scored the try of the year against the Eels on the weekend.

Although as you’d expect, moving the ball around more often did create more errors, which increased by 17%, similar to the increase in passing but that’s just the cost of doing business to improve a teams attack.

On the run metres front, it’s a blanket increase of 2-3% under Payten, and the increases to Post Contact Metres could indicate players increasing their effort as they hit the line and trying to push through initial contact.

Finally, I wanted to look at how their kicking changed, which has seen a drop under Payten in total kicks and kick metres. This lines up with the above changes, showing the team passing the ball more and in better field position, reducing the need for long kicks (or kicking the ball at all) to end a set. Total kicks are down nearly 6% And when they were kicking, they are doing so more accurately and effectively – fewer kicks dead (down 45%) and more forced dropouts (up 63%).

Things have also improved defensively for the Warriors under Payten as well in a few key areas. Total run metres conceded are down 6.7%, post contact metres by opponents are down 9% and offloads have dropped 8.4%. Clearly Payten’s Warriors are putting more effort in defense, reducing opponents gains after contact, and wrapping up the ball carrier more effectively.

And Payten has achieved all of this with a similar line up to Kearney. If anything, you could argue he was dealing with a weaker hand – Ken Maumalo, David Fusitu’a, Agnatius Paasi and King Vuniyayawa all returned home mid-season. They were replaced with loan players like Jack Hetherington, George Jennings, and Daniel Alvaro, who have been fantastic additions but came without knowing the structures and combinations the departing players already understood. Despite these issues, their discipline has improved with 15% fewer penalties conceded per game.

The fact that he has been able to squeeze this extra performance out of a squad with significant challenges living away from their families is incredible. Next season he’ll inherit a stronger Cowboys squad that desperately needs a new direction and an injection of belief and creativity. Payten has shown in just 11 games so far this season that he’s just the man to deliver all three, and it’s encouraging to see North Queensland give him a chance instead of giving a problematic and divisive former coach another run around.

The Eye Test’s Most Adequate of 2020 – Set restarts: if you’re not cheating, you’re not trying

This article was originally posted as part of NRL Round 9 notes and trends, July 14, 2020.

In last weeks trends and notes post, I showed there was a negative correlation between set restarts and margin, which had been positive from Round 3 to Round 7. With another week of matches completed, I thought I’d dig a bit deeper into this to see if I could find out what was leading to this.

The reason I find this so interesting is that it doesn’t conform with traditional rugby league thinking. Possession is treasured, and statistics like run metres correlate with winning games (keep in mind correlation doesn’t equal causation). Yet by giving away more set restarts, you’re giving possession and therefore more metres to the opposition. Surely that would result in giving up more points?

Looking at net margin plotted against net set restarts after Round 9 shows a similar chart to last round. I’ve named the quadrants as well to make it easier to identify what the chart is showing and the bigger the dot the more set restarts conceded.  

As with last week it appears that “winning” the set restart count is inconsequential, with only two teams having any significant net margin despite coming out ahead with set restarts. The top left quadrant – “Conceding and winning” – is the one I want to focus on though, given the makeup of teams within that area.

Last week there was only one top four side in that top left quadrant, which was the Panthers. This week they’re joined by the other top four sides – Melbourne, Parramatta, and the Roosters – indicating that giving away set restarts is a genuine part of their strategy. And that is only counting the set restarts given, not intentional slowing down of the ruck that isn’t called.

The Panthers are a curious case. Penrith not only have the largest difference between set restarts conceded and awarded at -23, they have also conceded the most in the NRL at 43 and been awarded the fewest at 20.

I noted last week that the only time they’ve come out ahead in set restart differential (Round 6 against the Eels), they lost by 6. This continued in Round Nine, with Penrith conceding three more set restarts than Cronulla, yet still beating them by 32 points.

In addition to the Panthers, among the other top four sides, the Roosters have the second fewest restarts awarded (23), the Storm are third fewest (24), while the Eels sit seventh (27).

With the limitations on publicly available data it’s hard to see exactly why they’re benefiting from these restarts, but we can use what is available to craft some ideas. One theory is that the early set restarts that are in vogue help the defensive team get set, limit chances for the team with the ball to gain momentum and exploit any breaks in their line.

A way of quantifying this is could be by examining the amount of runs under and over 8 metres conceded against the raw number of set restarts. As a team concedes more set restarts, there is a small positive correlation with runs shorter than 8 metres conceded (top of chart below), and a small negative correlation with runs longer than 8 metres (bottom of chart below).

Given this correlation, you could assume that the more set restarts you concede, the more likely you are to give up shorter, ineffective runs than longer more damaging runs.

This makes sense – giving up a set restart on the first or second tackle on your opponent’s 20m line and contain them within their own half is preferable to allowing them to string together a number of longer runs and push into your territory for an attacking kick or set piece. It also enables teams to maintain a defensive structure and limit any gains from broken play.

Examining when set restarts are given and the outcome of the consequent set compared to the average set would show if this is successful or not, but again we’re limited by publicly available data.

On the other end of the scale, the Bulldogs position on this chart is just another sad indictment on their run under former coach Dean Pay. They play a very conservative brand of football, limiting defensive mistakes and (attempting to) maintain possession and complete sets. As much as he has been able to get his players to show up every week under trying circumstances, this style of play hasn’t yielded any results and the constant switching of combinations appears to be actively hurting their performances. And let’s not even talk about the Queensland sides.

I’m not arguing that conceding another set of six to their opponent is the reason that the top four sides are sitting where they are. But there is something in the new ruleset for rugby league that is widening the gap between the haves and have nots.

At a surface level there’s a minor increase in win percentage if you lose a set restart count. Removing drawn games and even set restart counts, teams who had a negative set restart differential have won 56% of their games this season. In Round 9, six of the eight games were won by teams with a negative set restart differential. The only teams that won with a positive set restart differential on the weekend were Souths and St George Illawarra.

Conceding more set restarts to your opponent isn’t going to win or lose games for you, but strategically conceding them appears to be part of the game plan for the successful clubs in 2020.

The Eye Test’s Most Adequate of 2020 – Why volume statistics aren’t always your friend

This article was originally posted as part of NRL Round 15 notes and trends, August 25, 2020.

This week should have been a fantastic matchup of halves, with Shaun Johnson of the Sharks facing up against the Panthers and Nathan Cleary. Cleary has been one of the standouts this season, whilst Johnson is having a great season, but you wouldn’t necessarily know it from the way he’s often covered by the mainstream media. I’d pointed it out previously with a radar chart comparison in early August:

Johnson ended up missing the game due to some minor injuries and the birth of his child (congratulations Shaun!) and Cleary had another strong showing. Despite this we can still have a look at their statistical output for the season and use it as a test case for counting stats and raw volume statistics not telling the full story.

If you’ve been following for me for any length of time you know that I’ve put together some advanced statistics for rugby league, as using raw numbers mean players who spend the whole 80 minutes on the field usually dominate. If you’ve not read them, I’d recommend checking out my articles on Run %, Tackle % and Involvement Rate on the website, as they’re all incredibly useful in identifying high

But back to the topic at hand. It’s lazy analysis to only use counting stats without context but is more palatable to the wider viewing audience so I’m not going to deride them for dishing up what the consumer wants and easier to digest in small doses.

Source: Fox Sports Stats

Comparison of raw numbers – Cleary seems ahead. More passes, more runs, more kicks, more attacking kicks, more tries, more try contributions, more line breaks and more line engagements. Johnson is only ahead in try assists (20 to 14) and weighted kicks (17 to 10).

Per game stats will give a slightly better comparison, although it paints a better picture for Cleary who has only played 12 games compared to Johnson’s 14.

When players are compared on the usual pregame shows, it’s assumed that all players in a certain position play a similar game or similar role within a team, leading to scorching hot takes like this on social media from mid-June:

And if you look at the raw volume or counting statistics at that time without any context you’d probably agree – Johnson isn’t impacting the game as much as Cleary is.

But there’s one variable that’s not usually discussed (although the wonderful Jason Oliver pointed it out in his Round 15 preview on SportsTechDaily, another must read each week), is possessions. The amount of times a player gets his hands on the ball plays a massive part in his statistical output. The basketball adage of “you can’t rebound the ball out of the basket” can be applied here with a twist, you can’t do more in attack in rugby league without the ball in your hands.

For the season Cleary has 964 possessions, compared to just 733 for Johnson, a difference of 231 possessions. On a per game basis, that’s roughly 73 possessions for Cleary and 52 for Johnson. Cleary has his hands on the ball nearly 30% more than Johnson on a per game basis.

Knowing this, what if we looked at the same statistics again for Cleary and Johnson on a per possession basis. Would it show anything?

Source: Fox Sports Stats

Not initially, as those numbers are essentially meaningless – 0.016 line break assists or 0.187 line breaks per possession isn’t really meaningful. You can’t create 20% of a line break.

Instead we’ll normalise it to take out any bias that having more possesions per game creates. I’m going to pick a set number of possessions in between both players to even things out. The number doesn’t matter so much as long as we use the same number for both, and for this exercise I’m going to use 60 per game, since it’s a nice round number that falls between both Cleary and Johnson’s season average.

Source: Fox Sports Stats

Now we can see that they’re not performing that differently. Cleary has an edge with kicking, especially long kicks, whilst Johnson leads on weighted kicks and try assists. Yet for all the calls that Johnson needs to run the ball more, his runs, passes and line engagements are very similar to Cleary’s. Does he really need to “do more”?

That leads into the other part of player this analysis their positioning and their role. As mentioned above it’s assumed that all #6s and all #7s should play identically but this is rarely the case.

This was shown in a great article by Jack Snape from the ABC showing the locations NRL halves are receiving the ball. If you apply the Eye Testtm during Sharks games you’d know that Johnson sticks primarily to the right side of the field, while Cleary tends to operate on both sides. The above article shows this, with the locations of Cleary’s touches coming evenly across the field whilst Johnson’s are mostly on the right. You could then argue that if Johnson had a similar level of freedom as Cleary, he would probably increase his raw statistics.

The other part of the role is not just what side they’re playing on but how often they’re involved and relied upon for their team. Cleary takes about 16% of the Panthers total possessions, makes 36% of their passes, 75% of their kicks, 66% of attacking kicks and 44% of line engagements. Johnson on the other hand, takes 12.6% of Sharks possessions, 29% of their kicks, only 52% of their kicks and 45% of attacking kicks.

This again ties back to role. Similar to a sport like Formula One, the Panthers play with a clearly defined lead half in Cleary, with Jarome Luai supporting him. The Sharks more often than not play with their halves on a closer to equal footing, a 1a/1b type scenario, with each either sticking to their side of the field or sharing in the playmaking duties.

Asking Johnson to “do more” or run the ball more won’t necessarily help his game. More stuff isn’t always better. Johnson will turn 30 in a few weeks and it could be that he doesn’t have the same explosiveness and has developed more as a player is now picking his spots and interjecting himself at the right time. Just because he’s not shredding teams anymore with one of his explosive runs and making defenders look as if their feet are stuck in cement doesn’t mean he’s not impacting games.

Whatever the reason, the outcome of this specific comparison of Cleary and Johnson is that they’re both having amazing seasons for their team, and the difference in their statistical output is simply down to the role they play for their side, which can be accounted for by looking at a per possession basis.

Claiming one is better based on one number or a group of counting statistics won’t prove anything other than teams and players are different and may play different styles. Hopefully we’ll see some more analysis based on possessions than just counting stats moving forward.

Cover image – “Nathan Cleary” by NAPARAZZI is licensed under CC BY-SA 2.0

NRLW Advanced Stats – 2018 and 2019 seasons

If you’re a regular reader of the Eye Testtm, you’ll be (somewhat) familiar with the advanced statistics I use to analyse players throughout and across each NRL season. And now, they’re also available for previous NRLW seasons.

What do these statistics show? The issue is that generally middle forwards don’t play big minutes or put up big numbers and go unnoticed besides the odd comment about how much of an impact they make. To do so I created three advanced statistics for rugby league – Tackle %, Run % and Involvement Rate.

  1. Tackle % estimates the percentage of opponent plays whilst on field where a player completed a tackle.
  2. Run % estimates the percentage of team plays where a player completed a run during their time on field.
  3. Involvement Rate combines them and estimates the percentage of total plays a player completed a run or tackle whilst on the field.

If you want to read more about them, I’ve linked the explanations of them from the site.

Now I’ve explained them, let’s see how NRLW positions compare for these statistics to their NRL equivalents and look back at some top performers within each statistic from the past two NRL seasons. I would like to note that we’re dealing with some very small sample sizes, and even with a lenient minute restriction of 40 minutes I would still take these as indicative rather than representative of performances. Also, “current team” may also mean “previous team” for someone not playing NRLW currently, which is one of the issues looking at multiple seasons of data at once. Anyway, lets move on to the analysis.

Tackle %

There’s not a huge difference between the NRL and NRLW for Tackle %. Hookers and Locks are making tackles at the same rate, around 25-25%, indicating they complete a tackle on one in four defensive plays. Props and interchange players still sit over 20%, but around 4% lower than their NRL counterparts. This could indicate that a lot of the basic hit up work could be centered less on the middle of the field.

Positions on either edge of the field are similar as well, other than Second Row being down over 2% and Five Eight being up almost 2%, which given that they defend in the same spot could cancel each other out. Fullback is slightly higher as well at 4% for NRLW compared to 2.8% for NRL.

Who are the top NRLW players for Tackle % then? Below are the top 15 players by Tackle % for seasons 2018 & 2019 who played at least 40 total minutes.

Kate Haren formely of the Dragons leads the way with a Tackle % of 43% from her two games, indicating that she made a tackle on two out of every five defensive plays whilst on the field. Aliti Namoce who last plaeyd for Roosters placed second at 31.45% and Talesha Quinn who also last played for he Dragons came in third at 31.32%.

Rebecca Young from the Roosters was the only other player to have a tackle rate above 30%. As noted above, the average Tackle % for middle forwards is about 20-25% so each of these players are tackling well above average for their position.

One of the interesting differences here compared to the NRL is seeing Second Rowers at the top of this list, which is usually just middle forwards for the NRL. In addition to Namoce, Lorina Papali’I (29.38%) and Holli Wheeler (25.93%) also make the top 15 whilst playing in the second row. Again, the average for second rowers is 16.2%, which puts Namoce’s tackle rate almost twice as high the average second rower.

Run %

Looking at the average Run % across positions for the NRLW against the NRL, there’s not as much variance as there was for Tackle %. Differences fall between 0.5% to 1% for most positions, and the only significant change is at lock, where NRL players make a run on 10% of plays, whilst NRLW locks make a run on 7%.

So, who has the highest run rate among all NRLW players who played at least 40 minutes across 2018 & 2019?

Ngatotokotoru Arakua takes first place with a Run % of 20.92%, meaning she completes a run on at least two out of every five plays the Dragons used the ball. Second place is Chloe Caldwell from the Roosters at 20.45%, the only other player above 20%. Another former Rooster in Elianna Walton picks up third place with a run rate of 17.99%.

With the average Run % for middle forwards sitting in the 10-12% range, anyone over 15% is putting in an elite amount of work.

It’s also worth noting that new Dragons signing Isabelle Kelly at centre was extremely close to making this top 15, sitting only 0.15% outside with a run rate of 12.04% which is exceptional for a centre.

Involvement Rate

Given that Involvement Rate is a combination of Tackle % and Run %, it would make sense that any changes we saw in the previous two statistics would be reflected here as well.

Involvement Rates for middle forwards are slightly down as was exhibited with Tackle % rates. NRLW Prop forwards suffer the biggest drop at 2% compared to their NRL counterparts, otherwise things are relatively consistent.

The top 15 players in the NRL for Involvement Rate from the 2018 and 2019 seasons who played at least 40 minutes are shown below.

Kate Haren takes top spot here, as she did with tackle %, with an Involvement Rate of 26.37%, meaning she completed a run or a tackle on one in every four plays whilst she was on the field. Ngatotokotoru Arakua came in second with an Involvement Rate of 21.97% while Chloe Caldwell rounded out the top three with an Involvement Rate of 21.42%.

Brisbane halfback Tarryn Aiken is also worth mentioning, sitting in 19th place with an involvement Rate of 17.25% in a list that is dominated by middle forwards. This is mainly due to her running game, as she sits just outside the leaders in Run % with a run rate of 10.23%.

A statistical look at some of the NRL’s most improved players of 2020

The 2020 regular season is winding down and there has been some talk about the most improved player in the NRL. It’s a great talking point for these later rounds as most games are dead rubbers. Are you really interested to watch the Queensland Toilet Bowl this Thursday evening?

The problem with examining players who have improved in the traditional way of looking at who scored more tries or who’s averaging more running metres this year is that it falls into the counting statistics trap. You have a number of statistical buckets. Every time you do something, a bucket fills up. If your bucket is filled with more “stuff”. If you have more “stuff” than last year, then you’re more improved!

But is that the case? The above approach only rewards players who play more games, or spend more time on the field, or have a change in role. What about players who are doing more with less, there by being more efficient?

One way we can do this is by looking at a players output on a per possession basis rather than just their raw volume. This way we can if they’re doing more or less with the ball every time they touch it, rather than just looking at the end result which may have come from twice as many handles of the ball.

Regular readers may remember that earlier this season I’d made a comparison between Shaun Johnson and Nathan Cleary, based on the fact that Johnson only touches the ball about 50 times per game, whilst Cleary averages about 75 possessions per game. By taking their per possession statistics and then normalising them by the same number of possessions, their per game output was remarkably similar.

Speaking of Cleary, he’s been bandied about as one of the more improved players this season. But if you look at his possession stats year on year, he’s doing more but not more efficiently. This year he’s averaged 75 touches, up nearly one third from 2019 (57 per game). As a result, other than line break and try assists, and kicking stats, almost every other statistic is down on a per possession basis for Cleary.

That’s not a knock either, as it’s obviously working for the minor premiers and only a fool would suggest Cleary isn’t anything other than the NRL’s best halfback for 2020. But if we’re talking per possession, then becoming “less efficient” isn’t an improvement. Is he doing more? Undoubtedly, just look at where the Panthers sit on the ladder. But more isn’t always more efficient and can falsely be equated with improvement.

So, for this exercise, we’re going to expand on the previous analysis and look at normalised (by position) per possession statistics for some players that I’ve identified who have had a marked increase in their output in 2020. By doing so we can see just how much their performance has changed by giving them the same level of possession as a baseline.

To use this method to look at who has improved, I’ve narrowed down a pool of NRL players who played in both 2019 and 2020 (sorry Jamal Fogarty), which ends up around 370 players. I’ve left out another 90 players who played fewer than 200 minutes in 2019 (sorry Tino Fa’asuamaleaui and Harry Grant), as their previous sample size was likely too small to draw any conclusions from. For clarity, I’m using publicly available data from Fox Sports.

Below is a table of just how many times per game certain positions touch the ball, broken down for 2019 and 2020 with a % change. This helps to show just how many times different positions get their hands on the ball, but also to highlights the increase in possession due to the set restart rule change.

Average possession per game by position, 2019 v 2020 seasons

One thing that needs to be considered is the increase in time in play due to the set restart change. Time in play is up by at least 5%, possessions are up 6%, runs are up 5% and run metres are up 4%. Interestingly average metres per run are down 1.6%. Everyone is running more but overall, not as far.

Given this, most players will likely see an increase in runs and run metres thanks to the ball being in play more often. If you’re seeing anyone with a “career high” average in runs or metres or the like, that isn’t higher than the percentages above it is most likely not an increased output.

There are also some players who’ve put up ridiculous increases in some statistics this year due to a change in role. Cameron McInnes is a great example. He played almost exclusively at hooker in 2019, but for 2020 has adapted to more of a running forward role in conjunction with time at dummy half. As a result, his runs per possession are up 330%, and his run metres are up 248%. Not improved, just a change in role.

This is more an indictment of how little hookers are running the ball than necessarily any improvement in McInnes’ game.  As a result, players like McInnes who have moved between positions that dramatically alter their statistical output have so been excluded. Jarome Luai would be another, who spent most of 2019 as an interchange player before graduating to a full time five eight in 2020, with his minutes and possessions each increasing by over 100%. It’s not like for like so he’s unfortunately excluded from this list.

The one downside is that normalising players output on a per possession basis takes any defensive performance out of the equation. That’s not necessarily a bad thing, as there’s fewer defensive As a statistics available and it’s not an aspect of the game that can easily be quantified by treating player individually. After all, you can’t miss a tackle if you’re so far out of position that you can’t attempt one. So, for this exercise I’ve used the Eye Testtm to rule out a few players from the most improved list since I can’t quantify it easily (sorry Matt Dufty and Kotoni Staggs).

With that out of the way, let’s look at a a number of players that have made significant statistical improvements on a per possession basis, normalised by the average possessions at the position they play.

Dylan Brown, Parramatta

It’s not much of a coincidence that the Eels struggles this season traced back to when Brown suffered his season ending injury. He was an integral part of the Eels attack this season, touching the ball 25% more than he did in 2019 as Parramatta favoured his left side of the field. The increase in possessions weren’t used to increase his playmaking role (both passes and offloads were down per possession on 2019), but to showcase his damaging running game.

Even with the increase in possession, Brown sported double figure increases in runs and run metres, with a breathtaking 170% increase in tackle busts per possession. Unsurprisingly this led to significant jumps in tries per possession (+30%) and line breaks per possession (+95%). He also took on more of a kicking role as well, increasing by 55% through both long kicks (+114%) and attacking kicks (+37%). All this shows just how vital he was to the Eels game plan this season and his presence has been sorely missed. And that’s not even taking into account one of his overlooked abilities, usually underdeveloped in young half – his defense. But as stated we’re not able to quantify that as easily so we only have the Eye Testtm to use there, and he’s definitely above a passing grade.

Taniela Paseka, Manly

With both Manly starting front rowers missing time this season and the Sea Eagles sporting a thin interchange bench, Paseka has been required to increase his time on field in 2020 (28 minutes per game to over 34). He’s also getting more involved, with a 32% increase in touches per game, from 8.3 to 10.9. This increase in role has seen his numbers explode across the board as you can see from the above table.

Runs (+6%), run metres (12.1%) metres per run (5.7%) and tackle busts (+35%) are the bread and butter of a middle forward and Paseka has seen steady increases in those statistics. But it’s the improvement of other aspects of his game that have made him stand out. He’s become more of a passer and creator for the Sea Eagles, with a 360% increase in offloads per possession and triple digit increase in general passing. The most pleasing part of this is that it hasn’t come at the expense of his ball security, with Paseka’s per possession error rate down 75%. As the club prepares for current starting prop Addin Fonua-Blake to depart, Paseka has shown he can more than handle the load.

AJ Brimson, Gold Coast

After a nasty back injury, Brimson has made a full time move to the #1 jersey in 2020 and he’s been an integral part of their late season improvement. He’s played the full eighty minutes in every game this season after averaging 68 per game last year, and his involvement has increased slightly as well from 26.1 touches to 30.4 per game. With less time spent in the halves, he’s distributing the ball less but running more, with runs (+3%), metres (+30%) and metres per run (+26%) all showing dramatic improvement over 2019.

His ability to break the line and set up for others has been another marked expansion of his game with an 182% increase in tries per possession, and triple figure increases in try and line break assists. He’s also taking on some of the hard work this season, as his one pass runs have increased by 26%, and he’s busting tackles at a significantly higher rate as well (+35%). With two new star signings and having Brimson and Fogarty at the start of the season the Titans look to be one of the darlings of the 2021 season.

Sione Katoa, Cronulla

The Sharks are headed to the finals this season, and Katoa has been one of the few constants in a backline ravaged by injuries. With wingers usually playing the full 80 minutes his time on field hasn’t changed a lot, and his possessions per game are only up 10% on 2019, from 15.2 to 17. It’s what he’s doing with them though that is making a difference for Cronulla.

Katoa is running the ball 10% more per possession and producing almost 4% more run metres on the back of runs  with a distance of 8 metres or more increasing by 13%. He’s also taking a lot more basic hitups, with one pass runs up 30% per possession, and also offloading the ball slightly more (+5%) even though his general passing is marginally down. It seems like Katoa is picking his spots better as well, with errors declining by 26% despite offloading more, which usually leads to an increase in errors.

The biggest change in his game this year though is the ability to get over the line. Sure, it helps when you have Shaun Johnson playing inside of you whilst having one of his best seasons as well, but Katoa has emerged as one of the best finishers in the NRL. He’s scored 15 tries in his 17 games this season, which is an increase of 39% on a per possession basis. Line breaks are up by 33% too as they tend to go hand in hand with tries. How he’ll fare in 2021 will be interesting to watch as Johnson will likely out the entire season (or at best the majority of it) due to a devastating Achilles injury.  

Moses Leota, Penrith

Leota has been an important factor in Penrith’s ability to push through the middle this season. His minutes are up only slightly (an extra 2 minutes per game), and his possessions are actually down this season (-3.2%). His impact off the bench has been notable, with 15% fewer runs that were shorted than 7 metres, and 30% more that were longer than 8 metres.

He has seen double digit increases in runs per possession (+11%), run metres (+24%) and metres per run (+12%). His passes and offloads are down, as he’s playing a more basic game through the middle now. This has resulted in offloads and passes declining (-73% and -55% respectively), but it’s also meant the errors have come out of his game (-53%).

Jordan Pereira, St George Illawarra

Whilst he hasn’t had the same highlight season as the Dragons other winger, Pereira has been one of the most consistent Dragons all season. And he’s done all of this without being able to cross the line, with just one try this season and none since Round 6. He’s still been an excellent contributor, only seeing a 1% increase in touches this season and pumping out an additional 18% more metres per possession than last year.

And it’s not just short runs either – his metres per run are up 16% and runs longer than 8 metres have increased by more than 24%. He’s one of the hardest working wingers in the NRL, sitting 6th among all backs (including fullbacks) for total one pass hit ups, constantly helping the Dragons return the ball out of their own area. If he’d only been able to cross the line a few times this season he’d be snaring more headlines.

Sitili Tupouniua, Sydney Roosters

He’s reaped the benefits of playing on the best side in the NRL (ladder position notwithstanding), and whilst you could use that to refute his improvement, you’re not going to post increases like Tupouniua has without effort. With the injury toll the Roosters have suffered, he’s had to play an increased role, jumping up from nearly 30 minutes per game up to over 57, with a related 55% increase in touches (6.4 per game up to 9.7)

It has affected his running stats, with runs (-15.5%), run metres (-23.9% and metres per run (–9.9%) all suffering on a per possession basis but in doing so he’s become a much more damaging situational ball carrier. When you consider how many metres the Roosters get from their backs, James Tedesco especially, it’s less of a reflection on Tupouniua than how the Roosters play.

His offloads are up nearly 24%, and tackle busts have improved a similar increase (+29%), showing he’s moved from a one-dimensional meter gaining runner of the ball. Tupouniua has had a knack for finding the line this season (+209% per possession) but only has a 3% increase in line breaks, indicating that the Roosters may have found that the best situation to use him in is close to the line where he can crash over and not further out.

Luciano Leilua, Wests Tigers

The younger Leilua joined the Tigers this season from the Dragons and has developed into a damaging 80-minute edge forward. His minutes have shot up from 41 per game to 78, but hasn’t seen a corresponding increase in touches, going from 11.7 to 13.8 per game. Yet on a per possession basis, he has expanded his running game.

Runs per possession are up 20%, with run metres up 11%. His metres per run have declined slightly, down to 8.2% form 9.2%, which is also shown by a sizable 64% increase in his runs of less than 7 metres. It’s a Tigers wide problem as I’ve noted twice previously. He’s also been one of the few Wests players that has shown the ability to break through the line, as he’s nearly doubled his try scoring output per possession. With some additions to their middle forward rotation in 2021, the Tigers might have the go forward required to create more space for Leilua to cause more havoc down the left edge.   

Sam Stone, Gold Coast

Another player who hasn’t been playing the full season for the Titans but given his improvements over 2019 he could be playing a larger role in 2021. Stone hasn’t seen a substantial increase in minutes (68 to 71) and his per game possessions have dropped 20%. Yet almost all of his per possession statistics have seen growth on last season. He’s running the ball 7% more, resulting in 29 % more metres and an increase of 20% on his run per metre average (7.3 metres per carry to 8.8).

Stone is also busting tackles at an incredible rate compared to last season (+106%) which is leading to longer runs, with those greater than 8 metres up 9%. He’s not a stimulating choice from a marketing point of view, but the Titans and their fans should be very happy with his progress after joining the club from Newcastle.

Honourable mentions – Peta Hiku, Brian To’o, Dylan Edwards, Brett Morris, Regan Campbell-Gillard, Thomas Burgess, Daniel Saifiti, Jahrome Hughes, Blake Lawrie, Jack Wighton.

Run %: The other half of quantifying player involvement in rugby league

This article was originally posted on Medium in November 2019.

In my first article on some new advanced statistics for rugby league, I looked at a statistic for rugby league called Tackle %. The aim was to quantify how often a player made a tackle during their time on the field.

As correctly pointed out in some feedback to the article, Tackle % will always skew heavily for middle forwards as they make most tackles during a game. Part of that is intended, as there are plenty of statistics to evaluate performance with the ball. Yet there’s very little to evaluate middle forwards other than some statistical buckets labelled “Tackles” or “Runs”.

Continue reading Run %: The other half of quantifying player involvement in rugby league

Involvement Rate: a metric for player work rate for Rugby League

This article was originally posted on Medium in November 2019

The basics of rugby league haven’t changed in over a century, it still revolves around either running the ball or tackling the player with it. If you’re on the field and not doing either of those things, then you’re often described as a passenger or labelled with the venerable “playing in a dinner suit”.

Coaches and media will often talk about how “involved” someone is or was during a game, but it’s very nebulous and never quantified or backed up with anything other than a player having “X” amount of runs and/or “Y” amount of tackles. What if we were able to quantify the amount of this “involvement” a player had?

Continue reading Involvement Rate: a metric for player work rate for Rugby League

Tackle %: attempting to quantify defensive involvement for rugby league

This article was originally posted on Medium in October 2019

When defensive effort or involvement is talked about in rugby league circles, especially in mainstream media analysis, it eventually comes down to who made the most runs and who completed the most tackles. On the defensive side of things, more tackles equals more effort and more impact, right?

Usually the players who would be talked about are those topping the tackle counts. Mahoney led the National Rugby League (NRL) in total tackles this season with 1,052 tackles in 1,739 minutes 24 games. McInnes led the NRL in average tackles per game, with 45.8 from 23 games. Does that make them the best defenders in the game?

Continue reading Tackle %: attempting to quantify defensive involvement for rugby league