About

HPN (Hurling People Now) was started in 2014 by Cody Atkinson and Sean Lawson, two Canberra-
based sporting obsessives.

HPN’s work has been featured or referenced in The West Australian, The Age , ESPN, The Saturday Paper, The Roar and BigFooty, to name just a few.

Most of HPN focuses on AFL coverage, but over the years HPN has covered local Australian football,
rugby league, rugby union, NFL, basketball, the Olympics, soccer and hurling (which was the impetus
for the site’s name).

If you want to get in touch with us, contact us via the comments below any article, email us at
hurlingpeoplenow [at] gmail [dot] com, hit us up on twitter (@hurlingpeople, @arwon,
@capitalcitycody) or via Facebook at facebook.com/hurlingpeoplenow.

If you want to read more about smart footy analysis, we reckon you should give these people a read:
The Arc
Figuring Footy
Onballers
Matter of Stats
Insight Lane
FMI
Squiggle
Footy Gospel
Plus Six One
Analysis of AFL
Ranking Software

Almost all the data for HPN comes from the following sources:
AFLTables
DraftGuru
Footywire

Unless cited elsewhere, the data for all draft information comes from Draft Guru (which is an
absolutely fantastic resource). The indispensable AFLTables forms the backbone of the on-field
statistics part of HPN.

Where we write about other sports, we try to reference information sources where possible. If we
have left something out, drop us a line and we will try to let you know the source.

HPN.

8 comments

  1. Hey HPN,

    Just going through the PAV as you have seen on twitter (@bikkyboo), and I have a few questions about the formulas (i.e. mine don’t match yours, so I have obviously missed something critical). Would be good to run them by you if you have a minute spare,

    Cheers,

    Liam

    1. Hi Liam – what program are you using to run it? And what are the values that you are getting – overly high or negative values?

      Cheers,

      Cody

  2. Hey HPN,

    I think the work you’ve done here is amazing. I’m wondering now if you somehow cross-reference you PAV and PAPLEY numbers to distribute salaries under the salary cap. We know hot draft pick have a high trade value, but the variability is too high to commit to large salaries. Yet a player like Yeo, who is West Coast’s most trade valuable player, probably warrants their highest salary given he’s 24 years old and has less performance variability going forward. Is there some formula that can be developed that indicates a player’s share of the salary cap.

    Cheers

  3. Hi
    I have started applying my Defender Model this year (optimised parameters over last 4 seasons).

    Essentially each team is allocated a strength in points and a Defence %. The predicted scoreline is Team A strength modded by TeamB Defence and vice versa. Add in blowout adjustments for both strength calculations and predicted scorelines and individual home state bonuses and you have my model.

    Based on previous season analysis Defender also self-selects the games it is more likely to win line bets, typically 1-2 per round. This is from analysis of an assumed normal distribution around the Defender margin with a standard deviation of the MAE, PLUS a reliability score based on how “winning” that team has been in previous selected games (and “losing” for the selected loser).

    The reason I am emailing you however is that for the games defender self-selected HPN scored only 4 wins from 12 games. However for the games NOT selected by Defender HPN scored 23 wins from 32.

    Defender scored 10 of 12 for its selected games, and 13 of 32 for the non selected (better off straight up reversing tip).

    I(s there something in this? Defender is about consistent Team form over previous 11 weeks, HPN is about players. Defender is able to pick out teams with consistent (winning or losing) for, and inconsistent team form seems to add credence to individual player model of HPN.

    Defender::::Champion Teams HPN::::Teams of Champions ?????

    Your thoughts?

  4. Just looking through the PAV’s and I wonder whether the formulae/methodology unreasonably privileges ruckmen? It seems to me that there are some glaring anomalies in the data, including Callum Sinclair as the 8th most valuable player in the league, Toby Nankervis being Richmond’s second most valuable player and Jarrod Witts at 14 in the league.

    I wonder if the formula unduly weights hitouts due to a (comparatively) small sample of ruckmen in the league? I would be interested to see what would happen if HO were excluded from analysis (or, better yet, only HO to advantage were used).

    1. Hi Dredge,

      The short answer is in order to produce ratings that plausibly credit the good rucks over the years, as well as to get satisfactory valuations across forwards midfielders and defenders, some lesser appreciated current rucks also end up fairly highly valued. To a certain extent this should cause us to reconsider their worth, I think.

      If we’re going to use the basic stats that are available to value other players, some of these rucks also fall out of thast as valuable.

      Hitouts to advantage aren’t available in basic statistics, what ruckmen generally are getting credited for are their hitouts, marking, 1%ers and clearances. Witts, Nank and Sinclair all do fairly well in those last two categories, generally being top 2 or 3 at their respective clubs (Sinclair as low as fifth for clearances but with more goals), but it’s their cumulative contribution in different areas which gives them such high value here.

      1. OK, interesting points. Maybe the modern ruckman really is pivotal and should be getting a lot more credit?

        Should also have said how much I like the PAV concept and the blog in general as well. Really provides a much better basis for analysis and discussion than the overly-simplistic analysis done in most of the mainstream media.

        Well done.

  5. Hey!
    Firstly great blog and thank you for the material. I have been reading through your PERT methodology and decided to give it a try myself to understand the concept better. I’m basically getting the same mPAVs as yourself, but im not sure exactly how your adjustments are predicting the offensive efficiencies per i50 that you are projecting. Round 18 for example, has all team projected to score greater than 1.63 points per i50, and I have the league avg as 1.53.. im sure i just got something simple wrong here, as the number of i50s im getting is not a problem.

    Hope you can help out

    Sky

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