Charting the reliance of playmakers on winning the yardage battle

Attempting to justifying Nathan Cleary libel

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As the old saying goes, “the forwards decide who wins, the backs decide by how much.”

During halftime of the semi-final in 2021* World Cup, as the Kangaroos trailed the Kiwis, 14-10 and approximately 200 running metres, I tweeted something to the effect of “Nathan Cleary has never won a big game without either James Maloney or the pack winning yardage by at least 300 metres.”

That wasn’t hyperbole by the way. I verified that very quickly against his grand final and Origin appearances. Australia won that game, 16-14, closing the gap to New Zealand to a deficit of just 25 metres for a career first for the bechinned one.

That may seem like an unfair accusation, and it is, but I also do not care about Nathan Cleary’s feelings about a now-deleted social media post. What is relevant for this edition of Stats Drop is that I have no idea what the rate at which playmakers win games with the yardage battle stacked against them. It may prove that Cleary’s conditions for winning a big game are typical, even good?

Let’s find out what the relationship is between the differences in running metres over a game (as a proxy for the fabled ‘platform’ built by the pack) and the performance of individual halfbacks and five-eighths.

This chart plots Z score, as the measure of production of individual halves (minimum 20 starts in those positions since 2020), against the average surplus/deficit of their team’s running metres.

In and of itself, this chart doesn’t prove much about the players themselves. There is a mild correlation, with an R-squared of 0.18 (zero is no correlation or random, one is perfect correlation). That is lower than I would have guessed, both unseen and having eyeballed the chart, but establishes a league average mark.

Perhaps the best takeaway is if you’re regularly getting superior yards but also getting inferior production, we can safely rule the player out of the elite tier of halves. The best case scenario is that they can serve as a foil for a better partner or are only included because they have had plenty of opportunities to fill-in for an Origin-calibre half carrying the team over the last five years.

It doesn’t tell us a lot about the connection between an individual player’s performance and how it correlates to the team’s performance within their specific dataset. On average, Nathan Cleary is basically always getting a lift but that average could cover a considerable variation of outcomes.

If we look within each player’s dataset, we can correlate game Z scores with yardage differences and calculate an R-squared (coefficient of correlation) for each player. We then infer that a higher correlation between yardage surplus and Z score means that player is more reliant on having a well built platform for their own performance.

To be clear, “inferior performance” is a classification label of the player’s production and not meant to imply that players get worse as their reliance increases. As established in the first graph, players generally get better as the yardage battle is more convincingly won.

Assuming that we all agree that a player being able to consistently produce, irrespective of his teammates’ performance, is considered a desirable trait, this graph meets the eye test and the gut feel. The top left quadrant represents players who have been historically considered “good”, current form notwithstanding, and players in the bottom right have not. I’ve drawn a line at Z score = 100, which is to indicate the notional “average” production for the purposes of breaking the graph into quadrants but I think there’s a zone below 80 (the highlighted region), on which Ethan Strange and Jamal Fogarty sit on the cusp and Sean O’Sullivan is just below, where players’ production becomes untenable for first grade, doubly so for a leading half. There are exceptions - Isaiya Katoa, whose first grade career still features a high proportion of nothing games for the Dolphins that I would expect will make up a diminishing part of his resume as time goes by (Ezra Mam’s career has had a similar trajectory) - but there’s recognisable players, including one player I refuse to mention by name, but no stars in that higlighted region.

Nathan Cleary is not labelled but he is the dot next to Jahrome Hughes in the lower reliance / superior performance quadrant. As these are regular club games and not “big games”, using whatever definition of that term best suits the argument I’m making for the purposes of getting faves on a dead social media platform, it doesn’t invalidate my point but gives you a better context. We’ll have more on these two after the paywall.

The results are still not as clear cut as it seems. There is some overlap between player Z score (a component of which is running metres) and team running metres, but you would expect that to have a relatively low impact on the conclusions being drawn here because of how little bulk metres matters to being a half.

Without going into the hundred-odd individual datasets, we don’t know exactly much of this is determined by weird outliers or how diverse each dataset is. For example, Cleary and Hughes may still have datasets comprising almost entirely of yardage wins, which will look like low correlation because they are playing under almost identical conditions from week-to-week, and won’t tell us much about how they might perform in yardage losses. The league average standard deviation for yardage difference within each player’s dataset was 361, with a minimum of 242 (George Williams) and a maximum of 500 (Brandon Wakeham), suggests this effect is common enough across all players. Separating it out may prove difficult and it may be easier to live with it.

Still, I think taking both graphs together paints a picture about the production, performance and effectiveness of specific halves whether their team is on top or not.

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