NRL Fantasy is a game of statistics so we use a lot of them. Some of them are helping to understand Fantasy while some are helping us to understand rugby league itself and the teams involved. The statistics and projections we use aren’t perfect data so it’s important we make some disclosures. 

 

Statistics Sources

Throughout this Prospectus you will see lots of stats referenced. We rely on the NRL and NRL Fantasy statistics to get our information and there are two distinct sources that have some subtle differences.

The primary source of information is the NRL Fantasy stats which we have for 2014, 2015 and 2016. Generally, this is the source of information we are referring to. We also have historic information from 2005 to 2016. We refer to this information in our Hall of Fame article or when we are making references to a longer period of time. There are some differences between the NRL Fantasy and the Historic statistics:

 The Historic Stats include all finals series games, Fantasy stats do not.

 The Historic Stats also have quite a few gaps: o 2014 data is missing penalties.

-- ○ 2013 data is missing most of the line break assists, penalties and tackle breaks.

-- ○ 2005, 2006 and 2007 data is missing kick metres.

-- ○ All the historic data does not contain kick defusals or try saves. I have sourced all the positional information from team lists. 

 

NYC Statistics

We also have the 2015 and 2016 NYC stats. These don’t contain kick defusals or try saves. 

 

Projections

As part of every player profile we have included a projection for how we believe a player will score in 2017. Though we don’t profess to be wizards, there is some logic behind what we have done, as well as some context that is important to understand about the projection provided. So let’s cover that now. Agreeing or disagreeing about projections is fine, but it will still be a useful way for you to think about player performance.

Projected role

Whether a player is an 80-minute hooker or a 20-minute interchange player has significant implications on a fantasy score. So, for every player we have stated the role in which the projection relates. These are stated with the numbers representing minutes and the letters representing the position, for example 50/FRF means a 50-minute front rower, while 20/INT would represent a 20- minute interchange player. If the player doesn’t have the role we have projected them in don’t expect the projection to be accurate, they are role specific. The positions are on the field and not fantasy, wing and fullback have been split up as have second row and lock, because although these are the same fantasy positions, the on-field role and thus fantasy scoring is very different. Renegades Fantasy Sports NRL Fantasy Prospectus 2017 20 We wanted to project every player and this meant projecting players who had a very low likelihood of actually playing. Rather than misleading people about how good an option Matt Frawley is, we decided to state the level of risk associated with the role Frawley has been projected in. The broad definition of risks is:

 Low – the player has a near certain position and minute role.

 Medium – the player has some certainty on their position but minutes are unclear.

 Moderate – the player is competing for a role

 High – the player is a depth player only and unlikely to see significant game time without lots of injuries.

 Very High – the player is unlikely to play first grade, but you never know……

Projection points

We take lots of factors into account with the primary factor being past performance in the projected role, the more instances a player has played in the specific role the lower the risk of the projection, while the opposite is true for players that have never played in the NRL. We have also considered factors like team changes, point per minute changes, injury impacted scores, fluke scores, etc. The broad definition of risk is:

 Low – the player has a large sample of games and there is little reason to expect a large improvement or decline.

 Medium – the player has a reasonable sample of games, but there is room for some fluctuation to the projection.

 Moderate – there is some basis for the projection, but there are some heightened risk factors; for example extending a PPM over greater minutes.

 High – the player has very little or no recent first grade history, so the projection is based on limited information or on other players filling a similar role.

 Very High – your guess is good as mine. 

 

Advanced Statistics

In each team chapter you’ll see a table that looks like this:

There’s a few categories there that might be unfamiliar to some people, they are:

 Pyth wins

 Over/under

 Sch Str

 Close Game %

Pyth Wins

The Pythagorean expectation is a formula based on a team’s points scored and points conceded to come up with an expected number of wins. In the NRL all that matters is the W but the expected wins is a measure of the quality of play. The calculation certainly makes some intuitive sense; if two teams played all the same opponents but not each other and posted the same number of wins we would probably say the best team was the one that had the best point differential. Good teams don’t just win close games, they blow out other teams.

Over/under

When analysing this Pythagorean expectation, what we are really interested in is teams that greatly exceeded or greatly underperformed against their expected number of wins. That is what the over/under represents. A positive number is a good indicator, they were better than their wins. A negative number is a bad indicator, they were worse than their wins.

Close games

Although people can be loath to admit it, luck plays a massive part in sporting success and this often shows up in results in close games. When games are decided by less than one score the difference between winning and losing can easily be incredibly small. A team’s record in close games one season has no correlation whatsoever to its performance in close games the following year.

So that means teams that have a high winning percentage in close games probably aren’t as good as their standing on the table represents, while teams that have a poor record in close games are probably a bit better.

Here is a fascinating fact: the sides with the two worst records in close games in 2014 had the best records in close games in 2015. The Rabbits went from a 1-5 record to a 5-1 record and the Cowboys went from a 3-7 record to a 7-2 record.

Strength of schedule

The current NRL schedule requires a team to play all other teams once and nine teams one further time. Because there are a large number of common opponents, the gap between the hardest and easiest schedule is not huge. But, if there were two very similar teams and one had the hardest schedule and the other had the easiest, then the team with the hardest schedule can probably be expected to lose one more game than the team with the easy schedule. It's not a big impact, but one win can easily push a team in or out of the top four or the play-offs.

I prefer to use expected wins rather than actual wins in calculating schedule strength, although it doesn’t make a huge difference.

You can find more on these stats here and here. But they will also get explored in various team chapters.