Hopefully you've had a chance to look at the new player dashboard which has launched this week and can be found here or by following the link on the menu bar above. I'm sure that for the most part the data is self explanatory but I thought it might be useful to quickly run through the new features so you can get the most out of them. Let's start with the points section:

1. Here we simply see the players' actual points by week plotted against their expected total. One key to note here is that the expected number is based on their

2. This is a somewhat crude depiction of how each players' points total was earned. You can hover over each slice of pie for an explanation, namely:

4. As with the points chart above in (1), this chart plots actual goals scored against expected goals, again based on the actual shots registered by the player in a given game. Comparing these on a one-game, weekly basis is probably not a great idea, but over a longer period we can identify players who are perhaps getting a bit unlucky and under-performing their underlying stats and thus might be undervalued by the market.

5. These simple pie charts show the split between:

7. These charts highlight:

1. Here we simply see the players' actual points by week plotted against their expected total. One key to note here is that the expected number is based on their

*actual*shot data rather than the forecast number that will be given each gameweek starting with this one. Point being, the expected number shows how many points we'd expect a given player to score given all the other events observed from his performance.2. This is a somewhat crude depiction of how each players' points total was earned. You can hover over each slice of pie for an explanation, namely:

- Appearance (less yellow and red cards): blue
- Goals: green
- Assists: orange
- Defense (clean sheets less points lost for conceding 2+ goals): red
- Bonus points: Yellow

3. The +/- score quickly shows whether a player is under or over-performing his expected points total. A positive number suggests he has

*under*performed his total and thus should be due for some positive regression should he continue to produce shots / create chances etc at a consistent rate. Note that we're not saying that this gap will necessarily closed, only that we'd expect his future totals to match more closely his expected numbers.5. These simple pie charts show the split between:

- pSiB% - the percentage of a team's shots inside the box that the given player has accounted for (adjusted for the time actually spent on the field)
- pSoB% - the percentage of a team's shots outside the box that the given player has accounted for (adjusted for the time actually spent on the field)
- SoT% - the percentage of the player's shots that hit the target. Most research I've performed suggests this is a sustainable skill and won't regress to a league average rate, though we might expect it to regress to a player's own historic rate (a player can't for example, hit the target with 80% of his shots over a sustained period).
- SiB% - the percentage of a player's shots that were taken inside the box.

6. A player's goals per shots on target rate is somewhat complex but in the majority of my research I've found that for the most part it tends to regress towards something of a mean. Some players - though not particularly the first ones you'd think of - have show an ability to exceed the league average with some consistency, though I haven't done a full enough test to determine if these are simply expected statistical outliers. For now then, we have two different graphs to show. For lower profile players we can see their G/SoT rate for this season against the league average rate for their position. For the more established players, or those who bring a strong pedigree of success from other elite leagues, we've highlighted their historic rate as the comparison. Where available a bias is given to (i) time played on the current team, then (ii) time played in the Premier League, but we've used other league data for players like Ozil or Soldado on the assumption that these elite few need

*some*recognition as being better than simply average.7. These charts highlight:

- pCC% - the share of his team's created chances for the given player
- Final third passes - the percentage of a player's passes made in the final third of the pitch

8. The assist per created chance rate works similarly to the G/SoT rate described in (6) above. The key difference is the average rate comparison which for this metric is the rate for the player's team as a whole. For example, the above shows Aguero enjoying a 38% rate while City as a whole have seen their created chances converted at just an 18% clip. Allowing for some variance given the quality (and position) of pass made by Aguero versus some of his teammates, we might suggest that he's due for some regression in this area as the season progresses.

## 1 comment:

Hi, great to see the blog back in business :-) One thing I have a hard time understanding: Why does Remy have 7.3 expectancy next match. Much higher than his other matches and Liverpool should not be that favourable matchup, or?

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