Point per minute (PPM) is a term you’ll commonly hear referred to regularly throughout the season. Because players don’t all play the same amount of minutes a PPM makes players’ fantasy production relative to their minutes. This is a very useful tool in assessing the level of production a player could get in different levels of minutes. On a very basic level if a player has a PPM of 0.80 he would score 24 points in 30 minutes and 48 points in 60 minutes.

 

That’s the real basics but there are a lot of tricks in how you can best apply the information to help your fantasy team. 

 

Is a PPM static? 

In my experience the PPM of players in the forwards are impacted by two things: minutes and role. People like to think that players improve, and they do, but rarely does this have anywhere near the impact that minutes and role has. This is the reason you shouldn’t buy into the hype about a player looking great in pre-season because it probably won’t matter unless he has the minutes or role to capitalise on it.

 

Like their fantasy scores the PPM’s of backs and especially centres and wings can be all over the place because their scores can rely heavily on less consistent areas like try scoring. 

 

So how does increased minutes impact PPM? 

Increased minutes can lead to increased fatigue which in turn leads to a reduction in efficiency and a decrease in PPM. David Klemmer’s scores are a good example of this:

 

 

Each of the dots represent a game and show the number of minutes Klemmer played and the PPM he scored at. The line through the middle represents the overall decline in PPM Klemmer has as his minutes get greater. The steeper the line the more drastic a change in PPM, while a flatter line represents a player who might not see much change in PPM at all. Based on the line Klemmer could be expected to have 1 PPM in 30 minutes which would equal 30 fantasy points and about a .8 PPM in 60 minutes which would represent 48 fantasy points.

 

However, the data points aren’t clustered around the trend line which does impact on how much confidence you can place in the trend line’s accuracy. Part of the reason for this is because in Klemmer’s games of between 20 and 40 minutes he only had three games with a PPM above the trend line, while a lot of his 40 to 60 minute games are above the trend line. That’s really odd and it could mean the trend line projects Klemmer’s PPM too low. If I was asked to guess I’d go with a number slightly higher than the trend line. Regardless it demonstrates how PPM can decline in reduced minutes. 

 

How does role impact PPM? 

Props and locks have a much higher workload, mainly tackles but also runs, than edge back rowers. So, when a player who has played on the edge moves into the middle you could reasonably expect a jump in his PPM. Ryan James is the perfect example of this. His PPM has gone from 0.79 in 2014, to 0.51 in 2015, to 1.02 in 2016. That looks like it is jumping all over the place but when you look at his positional splits it reveals why:

 

 

In 2014 James only played four games and did a bit of prop, second row and interchange. In 2015 he played 20 of 21 games from second row and in 2016 he played all his games from the front row or interchange. Playing in the middle of the field is a significantly better role for fantasy scoring. 

 

Is past PPM relevant to the future?

Stats suggest that a player’s PPM one year is strongly correlated with his PPM the following year. The correlation for players that played at least four games in consecutive years between 2014 and 2016 is 0.858, which suggests a strong correlation between PPM performance between one year and the next. And when there is a big fluctuation it is primarily changes in minutes or role or random attacking stat fluctuations that is the contributing factor.

 

Here’s a scatter graph showing all of those players and their PPM compared to the PPM in the year before. 

 

 

This is a data set that has a pretty strong correlation and there are a lot of groupings around the trend line. Although that gives us some confidence that year to year PPM is pretty reliable it is really useful to have a look at some of the outliers to see what factors contributed to their change.

 

Here’s that scatter graph again with a few of the outlying data points identified: 

 

 

Let’s start with the players below the line, these are the guys that have increased their PPM compared to the previous year. 

 

Shannon Boyd 14/15 – I noted that in last year’s prospectus that I thought a lot of this was fluke as Boyd scored five tries in 2015 and just one in 2014. Was I right? Read on. 

Evans 14/15 – Improved play? This looks like the rare occurrence of a player making massive improvement in PPM while playing the same role and minutes. 

Dan Mortimer 15/16 – Mortimer spent a lot of time in the halves in 2015 and was entirely a starting or bench hooker in 2016 which meant his tackle count went way up while his minutes went way down. 

Lesson Ah Mau 14/15 – role change. Moved from second row to prop.

Ryan James 15/16 – I talked about James above.

RTS 14/15 – role change. Moved from wing to fullback. 

Nathan Green 14/15 – Green only played four games in each of these seasons but in 2014 he played two games at centre, one on the wing and one small minute interchange appearance. In 2015 he played an 80-minute game in the second row, two games from the interchange and another on the wing. By playing more minutes in higher work rate positions Green jumped up his PPM from terrible to slightly less terrible. 

 

Now let’s look at the players above the line, these are the players who decreased their PPM compared to the previous year.

 

Kane Evans 15/16 - Evans’ PPM would drop back to 0.95 in 2016 playing an extra 7 minutes a game but he was still a lot better than 2014 when his PPM was 0.791. Somewhere between September 2014 and March 2015 Evans learnt how to break a tackle. 

Shannon Boyd 15/16 – That sound is me being right. Boyd’s 2015 PPM was a fluke. His 2016 PPM was right back to where he was in 2014 because he didn’t score as many tries. 

Martin Taupau 15/16 – After playing prop and lock in 2015 Taupau would move to Manly where he was deployed mainly as an edge back rower. That leads to a drop in PPM.

Api Koroisau 14/15 – Koro went from playing interchange hooker to playing a lot in the halves.

Jake Granville 14/15 – In Granville’s first season in Townsville he received a lot of extra minutes. This saw a reduction in efficiency. 

Daniel Levi 15/16 – Levi went from averaging 31 minutes a game in 2015 to 55 minutes a game in 2016, like Granville this reduced his efficiency.

Aidan Sezer 15/16 – Sezer saw a huge reduction in his kick metres (from 299 to 217) because the Raiders offense was so dynamic he didn’t need to kick as much as he did on a putrid offense like the 2015 Titans. He also had a drop in run metres (74 to 42) because the Raiders offense was so dynamic why wouldn’t you just pass the ball to Blake Austin, Joey Leilua, Jordan Rapana or Jarrod Croker.

Steve Matai 14/15 – His already below average play fell off a cliff. Hard to play worse than Matai did in 2015. 

Brett Stewart 15/16 – It is hard to play worse than Matai in 2015 but Brett Stewart did it in 2016. His already poor play in 2015 fell down a cliff and then his body got sucked into the Mariana Trench. Manly fans hope that body won’t be resurrected on the 2017 Sea Eagles.

 

I think I know who the next player to breakout is but how will he score?

You’re pretty brave if you take this approach purely based on improving PPM (like Kane Evans) compared to a changed role (like Ryan James). Over the past two seasons only 22 players lifted their PPM by more than 0.20 (only three of those players were from the 2016 season). There’s also a lot of players who were still pretty fantasy irrelevant players in the year they made their jump. In fact, only Ryan James, Roger Tuivasa-Sheck, Joey Leilua and Glenn Stewart were players worth being in your fantasy teams. Here’s the list: 

 

 

And here is why the above list contains a lot of non-relevant fantasy players. Let’s say your player played 35 minutes last season and had a 0.75 PPM he would have averaged 26.2 and had a starting price of $235k. Here’s what you can expect from him over a number of PPM intervals: 

 

 

As you can see this shouldn’t be very enticing. Even in the highly unlikely scenario that you are right about the player improving like Kane Evans did he still only increases his fantasy average by 12.3 points and $113k. 

 

You really need to find players who are doing more than just increase their PPM which leads us right back to why a player’s PPM in one year is relevant to the next. The most likely scenario is a player’s PPM will continue at a pretty similar level to the one he had last year. 

 

Show us an example of a player with an expected decline in PPM as their minutes’ increase?

We can do a similar example as above. This player has excelled in limited minutes and had a PPM of 1 playing 20 minutes a game which put his starting price at $184k. We’ll lower his PPM as his minutes’ increase which will make him look something like this: 

 

 

With that sort of PPM this player is probably a front rower, so getting 60 or 70 minutes is pretty unrealistic but even at the 30 or 40 minute mark this player is useful for fantasy and probably not as hard to identify as a player who will make a sudden leap in his PPM.

 

Being able to apply a PPM combined with some ability to identify players who will see a big leap in minutes is one of the best ways to make sure you find the right mid-range talent.