If you’re anything like me, you’ve spent many hours – usually on a Thursday or Friday evening –  pouring over information when trying to decide who to play or who to captain for an upcoming round.

There’s many things that usually factor into these decisions. It’s been raining in Auckland, should I move the VC off Shaun Johnson? Should I play everyone I have who is facing the Knights or Titans? Jordan Rapana has the Tigers this week up against their defensively challenged outside backs, should I straight captain him? Cameron Smith is coming off five days rest, just how many unicorn points can he get?

Whilst you shouldn’t be looking at just one variable for team and captain selections, a good place to start is how likely they are to pick up a win.

Looking at Supercoach scoring from 2015-2017, playing in a winning side is worth 7.6 points per player. The average Supercoach player scores 49.1 in wins and just 41.5 in losses. This difference has been increasing as well, from 7.0 in 2015, to 7.8 in 2016 and 8.0 last season.

It becomes even more important if you look at just the top 40 total point scorers over that three-year period. The difference between a win and a loss jumps up to 11.9, and with 24 of the top 40 playing having a 50% record or better during that time frame it becomes an even more important factor.

When analysing the data, seeing a trend that attacking players have a significant difference in wins shouldn’t come as a massive surprise. They rely on attacking stats like tries, line breaks or assists to rack up those high scores, and these are typically scarcer in losses.

However, it’s the size of gap between them for some players that may raise a few eyebrows. Many them have dramatic differences based on results, while others are relatively unaffected, with a number of popular choices averaging fewer than 50 when their side loses.

Below are the top 40 scorers from the past three seasons, ranked by total points and split by averages and PPM in wins and losses. Anyone who is not playing NRL this year (e.g. Semi Radrada) has been removed. Everyone inside the top 40 has played at least 37 games over this period providing a sufficient sample size, with the lone exception being Billy Slater (26 games).


Player Win Avg Loss Avg Difference Win PPM Loss PPM Difference
Paul Gallen 78.56 72.00 6.56 1.18 1.06 0.12
Cameron Smith 76.61 64.80 11.81 0.98 0.81 0.17
Johnathan Thurston 82.46 47.57 34.89 1.04 0.59 0.45
James Tedesco 88.86 60.80 28.06 1.14 0.79 0.36
Shaun Johnson 89.62 56.03 33.59 1.12 0.71 0.40
Simon Mannering 69.12 70.93 -1.81 0.91 0.92 -0.01
Sam Burgess 78.06 65.14 12.92 1.11 0.91 0.20
Andrew Fifita 69.19 68.64 0.55 1.27 1.23 0.04
Nathan Cleary 78.59 54.00 24.59 0.98 0.67 0.31
Roger Tuivasa-Sheck 76.61 56.65 19.95 0.99 0.72 0.27
Anthony Milford 75.36 48.05 27.31 0.95 0.60 0.35
Tom Trbojevic 72.92 60.83 12.09 0.92 0.78 0.13
Cameron Munster 68.90 57.79 11.12 0.87 0.72 0.15
Trent Merrin 65.48 64.14 1.35 1.10 1.12 -0.02
Jason Taumalolo 63.50 64.19 -0.69 1.21 1.11 0.10
Billy Slater 63.50 64.50 -1.00 0.83 0.79 0.03
Jarrod Croker 73.03 51.42 21.61 0.91 0.65 0.26
Tohu Harris 64.12 59.83 4.29 0.84 0.80 0.05
Greg Inglis 69.62 55.16 14.46 0.89 0.71 0.18
Cody Walker 76.53 54.04 22.49 0.96 0.67 0.29
Jake Trbojevic 65.91 59.69 6.21 1.04 0.92 0.12
Jack De Belin 64.32 60.32 4.00 1.03 0.92 0.10
Boyd Cordner 63.00 59.83 3.17 0.83 0.77 0.07
Martin Taupau 63.65 59.81 3.85 1.20 1.05 0.15
Josh Papalii 67.15 55.13 12.02 0.88 0.72 0.16
Jordan Rapana 68.14 52.41 15.73 0.87 0.68 0.19
Bodene Thompson 66.74 57.26 9.48 0.87 0.79 0.09
Matthew Moylan 67.86 51.30 16.56 0.86 0.67 0.20
Aaron Woods 65.48 57.76 7.72 1.16 1.01 0.15
Jake Friend 62.71 57.82 4.89 0.89 0.77 0.12
Ryan James 68.50 55.13 13.38 1.07 0.84 0.23
Matt Gillett 60.76 58.74 2.02 0.79 0.79 0.00
David Nofoaluma 71.27 53.00 18.27 0.89 0.67 0.22
Gareth Widdop 78.53 43.06 35.48 0.98 0.55 0.43
Mitchell Pearce 63.82 51.95 11.87 0.82 0.65 0.17
Ben Hunt 66.64 45.57 21.08 0.87 0.57 0.29
Daly Cherry-Evans 64.28 54.32 9.96 0.80 0.70 0.10
Jesse Bromwich 58.22 60.12 -1.90 1.03 0.94 0.09
Michael Morgan 63.84 49.91 13.93 0.81 0.64 0.17
Bryce Cartwright 61.61 55.67 5.94 0.98 0.89 0.09


From above table you can see that Johnathan Thurston averages 82.5 in 35 Cowboys wins over the last three years with a healthy 1.04 points per minute. Yet in in 14 losses that drops to 47.6 with a paltry 0.59PPM. This gap of 0.45PPM is the largest inside the top 40, just ahead of Gareth Widdop with a 0.43PPM variance (0.98PPM in wins, 0.55PPM in losses).

Whilst Thurston has the largest PPM disparity, Widdop has the widest difference in average. The Dragons half knocks out 78.5 in wins, but drops to 43.1 in losses, a gap of 35.5 points. His average in defeat is the lowest of the top 40 players and given the average Supercoach player scores 45.3 points per game, Widdop becomes a below average selection in losses.

For those who like to live on the edge, Shaun Johnson averages 89.6 when the Warriors are victorious but just 56.0 in losses, which is the highest average from a winning side in the top 40. As for his score in losses, given how dire the Warriors have been in defeat over the past few years that’s not as terrible as I would have expected.

New Roosters fullback James Tedesco could be in line for some huge scores this season judging from this data. The former Tiger posted an average of 60.80 in losses over the past three seasons, but 88.9 in wins for a difference of 28.1. The king of tackle busts averages 7.1 in wins but only 6.1 in losses, as well as seeing an increase in errors and 10% fewer run meters. When you consider his record was 22-40 over this period, joining a loaded Roosters side could result in him being a must have at a stacked fullback position.

Anthony Milford shares a similar difference to Tedesco, scoring 27.3 higher in Broncos wins, not only suffering from fewer attacking stats in losses, but also a decrease in run meters by a 33%, fewer kicks and an increase in errors. He is also one of just four inside the top 40 to average fewer than 50 in a loss with an average of just 48.1 points per game.

Jordan Rapana is another from the rocks and diamonds club but aggregating his past three seasons masks some of his inconsistency. The Raiders winger averages 68.4 in wins and 52.4 in losses over this time yet averaged 13.7 more in losses in 2016. That is in stark contrast to his incredible rollercoaster ride in 2017, averaging an eye watering 93.7 in wins but only 53.8 in losses.

It’s not just the outside back who experience these increases in wins, as some edge forwards also benefit. Ryan James scores 13.4 more points per game in Titans wins, which is hardly surprising given his knack for crossing the line. Raiders forward Josh Papalii is another forward with a large fluctuation, with an average of 67.2 in wins but only 52.4 in losses. They may sit outside the top 40 scorers, but Cowboys pair Coen Hess (+10.6) and Gavin Cooper (+15.4) also fare better in a win thanks to their ability to score a try.

As expected as it was that attacking players scores fluctuate the most based on result, it is also equally unsurprising that the scoring from strong base stats players are less likely to change based on results.

Simon Mannering and Jason Taumalolo posted virtually identical PPM regardless of the result. They both actually average more in losses, +1.8 for Mannering and +0.7 for Taumalolo. The composition of their points is different though, with more tackling and fewer hit ups and run meters in addition to fewer attacking stats.

In addition to the above, a few more notable players score more in defeat. Paul Vaughan (+1.7) and Jesse Bromwich (+1.9) all score slightly more in losses, as do a few of the mid-range options, most notably Corey Haraweira-Naira (+6.0), Ethan Lowe (+4.3), Manu Ma’u (+4.3) and Dylan Napa (+5.2).

Billy Slater averages 1.0 more in Storm losses but given the scarcity of that happening– Slater has played in just 4 over the past three years in his 26 games – it’s too small of a sample size to take seriously.

There’s some excellent set & forget candidates amongst the forwards for the risk adverse. Andrew Fifita (+0.6), Trent Merrin (+1.3), Jack De Belin (+4.0), Marin Taupau (+3.8), Tohu Harris (+4.3), Boyd Cordner (+3.2), Jake Friend (+4.9), Jake Trbojevic (+6.2) and Matt Gillett (+2.0) all offer 60+ averages with excellent consistency irrespective of results. Sam Burgess may average 11.1 more in wins, but his 64.8 average in losses ranks only behind Gallen, Fifita and Mannering.

For outside backs, Cameron Munster is a relatively safe bet with only an 11ppg difference between a win and a loss, as is Daly Cherry-Evans with a 10.0 variance.

Whilst it might be stating obvious, the conclusion here is that that winning games certainly matters for players who rely on attacking stats to boost their scores. It matters less so for forwards who rely on base stats, and in some cases, it makes virtually no difference.

As mentioned at the start of this article, no one single variable should be the basis for a Supercoach decision. But If you’re in a situation where you’re looking at a coin flip for a captain decision or filling your last reserve slot, this data suggests going with players from a team that is favoured to win.

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vikorrOzmancarlosthedwarfHarFin Flyerscowboyrod Recent comment authors
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really good article carlos – thoroughly enjoyed it.
If possible it would be interesting to see the upside and downside around these average. For example SJ in wins averages 89.62 with x% above 100 and y% below 60 and in losses averages 56.03 with a% above 80 and b% below 40?


Nice one, Carlos. Great info.


The only problem now is using this valuable information when it’s so difficult to pick the winners of most NRL games, though it’s not as hard to identify games which are more likely to result in a blow-out to one team. And logically, these are the ones you really want because winning or losing may be less significant to SC scores for those players relying on attacking stats than the number of points their team scores. If someone had the time and inclination to do the research, it would likely confirm that most players have significantly higher SC scores when… Read more »

HarFin Flyers

Well written mate and a very enjoyable read. After reading this my creative juices started flowing re finding C/VC options and I ran some numbers on teams giving up SC points (2017 only and Rds 1-7 only). A little surprisingly (to me anyway), the Titans were the worst. They gave up at least 1 x 80+ score every week in Rds 1-7 last year. In total they gave up 21 x 80+ scores in the first 7 rounds, 18/21 of those were scored by BACKs. The Tigers were next with 16 x 80+ scores (at least 1 every week as… Read more »


Has this article convinced anyone else to pick Rapana? Consider:
– the Raiders easy start to the year,
– Austin is likely to go hooker, partly because he can pass right (Hodgson used to lob right, compared to a strong left side pass, so he would take on occasion take a left option when he should have gone right)
– Ricky had a full off season to iron out the kinks that developed last year