Our AFL Daily Fantasy Sports Tables (available for MoneyballDraftstars and PlayON) are updated on a weekly basis with up to date analysis giving you the edge against your competitors. The tables provide current season fantasy stats for every player as well as predictive analysis for the upcoming round. Continue reading to understand all the metrics and how this table can be used to identify players for your daily fantasy sides, whether it be for cash or tournament competitions.

Glossary

Player Name & Team – pretty self-explanatory
Opp – this coming week’s opponent
Salary – daily fantasy salary
Pos – daily fantasy position
Proj – projected fantasy score for the upcoming round
+/- (Plus/Minus) – projected fantasy score minus historical expected fantasy score for a player of same salary
Gms – total games played for current season
Ton – total number of fantasy 100’s for current season
PPM – fantasy points per minute for games played in current season
Avg – fantasy average for current season
Avg* – adjusted fantasy average for current season, excludes games where players have played less than 50% TOG, effectively removes games where players have been injured
S_Val – players adjusted fantasy average shown as a multiple of their salary
Form – fantasy average over last three games (current season only)
F_Val – players fantasy average over last three games shown as a multiple of their salary
Max – maximum fantasy score for current season
Min – minimum fantasy score for current season
TOG – average time on ground for current season, excludes games where players have played less than 50% TOG
Var – players consistency rating calculated via coefficient of variation, excludes games where players have played less than 50% TOG
Playing Status – identifies whether the player is named to play for the coming round or not

Examples of Use

Identifying Value Plays Based on Season Stats

Daily Fantasy Sports providers have different methods with regards to assigning salaries to players. For Moneyball in AFL it’s historically been seen that players are priced at ~11-12x their season average, for Draftstars this number is closer to 7x. Moneyball follow a more commonly recognised approach of steadily changing player’s salaries over a number of rounds until it closely resembles their intended multiplier, Draftstars however have shown they are willing to change player’s salaries markedly from round to round. Knowing these multipliers means we can easily identify under-priced players using our tool.
Firstly, by sorting the table by S_Val in a descending order we quickly identify those players who have produced scores so far this season in excess of what would be expected from a player with a similar salary. This will more often than not highlight rookies and other players who have played a small amount of games for the season (in Moneyball especially). It’s important to note here that this S_Val metric excludes any games where the player has been on the field for less than 50% of the time as injury affected scores can skew data.

The second simple way of identifying value is to sort on F_Val which is each player’s fantasy average over their last three games shown as a multiple of their salary. This view predominantly includes a large proportion of the same players as the previous sorting activity, however, it often highlights players that may have seen a recent role change resulting in a marked increase in average to a point where their salary hasn’t yet caught up. For example, the table below shows that Harrison Himmelberg over his last three games has delivered with an F_Val metric of 15.19x salary compared to an S_Val of 10.08x. This can be explained by the fact that Jeremy Cameron has been missing of late due to suspension and Himmelberg has taken on a larger role in the forward 50. Using this strategy can help you easily identify players that you may not necessarily have considered previously.

If you’ve identified a player through this method then it’s recommended to head over to our detailed Gamelogs page and take a look at the player’s stats to confirm there is a discernible pattern and it’s not just one large score that has caused the increase in average.

The value metrics explained here are an excellent tool for identifying under-priced players but it naturally leans towards cheap players, for example, for a top priced player on Moneyball to have a value of 15x he would have to be averaging 150 points which is extremely unlikely. That is why we recommend using the tools outlined above to help identify your value plays.
The value metrics can also be used in conjunction with our other tools to help analyse high priced players but you will find that their values are found to be a lot closer aligned as shown below for players priced at $10,000 or over.

As a quick visual indicator of the top ten value plays over their last 3 games we include a bar chart which allows the user to quickly visualise who’s delivered the best value to coaches in recent rounds.

Identifying Best Projected Plays

Knowing the limitations in the value metrics mentioned above (ie the natural tendency to favour cheaper players), we have used +/- (plus/minus) to ensure that all players are on a level playing field when it comes to our projected fantasy scores for the round ahead.

Plus/minus is the difference between the player’s projected score and the historical average score (taking into consideration ~2 years of fantasy data) that a player of the same salary has delivered. For example, Sean Darcy is projected to score 80.5 points in this example and historically a player who has been priced $4,800 on Moneyball has produced an average score of 57.88 points, meaning that Darcy is projected to score +22.62 points more than what you could expect for that salary.

Once again, as with the value metrics, we provide a visualisation above the table showing the top 10 projected plus/minus plays for the upcoming round.

Other Ways We Can Use the Tool

There are numerous ways this tool can be used to help in building your daily fantasy sides, in brief here are some others:

  • Identifying the Chalk – if you want to see who the chalk value plays are going to be then you can identify these pretty simply using the value metrics above. You can then decide on whether you want to go with the crowd or try gain an edge on the competition by fading them.
  • Identifying Tournament v Cash plays – in cash we normally look for lower risk plays and the Var column provides the coefficient of variation for each player with a lower value indicating that that player has been more consistent to this point in the season. A larger Var value may indicate that that player is better suited to tournaments if he has a favourable matchup. Matchup’s can be analysed using our AFL DashboardPoints Allowed by Position tools as well as other resources we provide.
  • Floor Plays – another one for cash games where filtering on Min will highlight those players that consistently produce high scores. We can also use the Ton column to see how many times a player has exceeded the 100 mark in the current season.
  • Sometimes we hear those snippets from coaches in press conferences that say “his fitness is improving and we’ll be increasing his time on ground this week”, using this information we can utilise the PPM and average TOG data to extrapolate out what we think a player will deliver with increased ground time.

There are obviously many other ways that this tool can be used and no doubt individuals will interpret the data in different ways using different strategies. Our recommendation is to use the data provided in conjunction with our other tools to ensure you give yourself the best possible chance at winning that tournament or cash game.

As Always, Gamble Responsibly