DFS Soccer: Shot-Creating Actions (SCA) metric

Metric under Analysis – Shot-Creating Actions (SCA)

What are Shot-Creating Actions?

The two offensive actions directly leading to a shot, such as passes, dribbles, drawing fouls, or any defensive action such as tackles and/or interceptions. A single player can receive credit for multiple actions and the shot-taker can also receive credit. To better reflect this metric within a span of a soccer match, you would refine it to SCA per 90 minutes to give you a better picture of production throughout the entire game.

Why look at this metric?

It is one of the best determinants of how a team operates and succeeds. This metric allows you to identify the main contributors to the success of their team. In other words, it signals which players make the difference on the field. The greater the chances are that a player ranks high in this category, the greater likelihood he will be involved in a goal-creating opportunity.

How is this relevant to DFS?

Very simple – goals and assists matter! They are the bulk of your overall score in any soccer slate, whether it be in classic mode or showdowns. If you fail to recognize this, you are taking the wrong approach. Not to mention, the factor of shots assisted on DraftKings and chances created on FanDuel. This metric enables you to potentially predict where the production will be coming from, especially after considering factors such as winning odds and set-piece takers.

Note: Just make sure there is enough of a sample size of games played (in this case, under the 90s column). I would say anything above four games, or 90s, is sufficient enough.

Can you illustrate this?

Below I will highlight a few examples. I, myself, enjoy using this metric a lot in showdown contests. There is no way of predicting the future but there are methods you can use to create a reliable and reasonable forecast of what to expect.

Note: I have crossed out non-starters in my exhibits.

Fulham (0) v. (1) West Ham EPL showdown – November 7th

As seen, Cairney and Lookman led the match-up with the highest SCAper90 for Fulham while Cresswell and Masuaku led the category for West Ham. This right here already provides you a starting point of who to look at for the Captain spot (1.5x the points). And if you further look into the odds of this matchup in which West Ham were slightly favored – there would be a lean to the West Ham players as your choice. This does not mean you have to always go that way, but it just indicates that the field is more favorable to the favorite. In other words, this team will create more chances and have a greater opportunity to score more than their opponent.

Here are the results (DK pts):
Cresswell – 18.78
Masuaku – 13.10
Cairney – 13.62
Lookman – 9.58 (with a missed PK)

Just from that, you can already see the effect of the SCA metric. And if it hadn’t been for Lookman’s missed PK at the end, the results would be even more in line to what could have been predicted. So where do the rest fall?

Bowen – 10.64
Robinson – 5.90
Mitrovic – 4.04
Fornals – 2.62
Coufal – 14.82
Zambo – 6.84

Do you see the drastic difference? Your core four could have easily been determined by the top SCAper90 producers which all scored a more consistent and higher score than most of the remaining players available for selection. Evidently, in showdowns, sometimes you require that one punt or outsider pick which in this case was Soucek who scored 18.38. However, without the solid floors and production of your top SCA manufacturers, you would most likely miss out.

Let’s move on to another example.

Chelsea (4) v. (1) Sheffield Utd. EPL showdown – November 7th

From the get-go, you can see that the production between these two teams are significantly different. Chelsea seems like a powerhouse in this game with super-high SCAper 90 totals while the same cannot be said for Sheffield where any one player struggles to get above the 2.0 mark. Also, given that Chelsea were huge favorites in this game, it would be obvious to try to build your showdown line with a stack of theirs. Therefore, inflate Chelsea’s line and deflate Sheffield’s statistics – that is another way to factor in odds. And of course, determining set-piece takers goes a long way. From example, early in Chelsea’s season, James was taking everything. Then Chilwell was introduced into the lineup and began taking the majority. Then Ziyech came into the mix and emerged as another set-taker. Thus, also knowing the trend and current status of who is on sets, will help you determine which way the SCA metric is heading. In other words, for James it would slightly be dropping while it would be rising for Ziyech.

So what were the results of the higher ranked players?

Ziyech – 28.86
Kovacic – 12.80
Chilwell – 21.42
Mount – 10.28
Werner – 17.20

Where did the rest fall?

Abraham – 20.70
James – 11.20
Berge – 8.74
Lundstram – 4.38

Once again, the top SCAper90 producers did not disappoint. Making a core four out of them and then adding some outsider picks along the way would have easily paid dividends. And as you could have forecasted, Chelsea was the dominant force in every category. The best ranked Sheffield player only reached 8.74 points, and that was with a lucky assist. You were more well-off if you decided to take a random Chelsea player at a lower price than banking on one of the Sheffield players to produce gains.

We will take a look at one more to increase our sample size.

Seattle (1) vs. (1) LA Galaxy MLS showdown – November 4th

Even in other leagues, we can see the effectiveness of this metric. Take the MLS for instance. This was also a game where a lot of regular starters did not play. This would make it a bit more challenging for anyone who doesn’t follow the game exclusively. However, there is one safe haven to this – the SCA metric.

Seeing the initial lineups, Seattle’s win odds diminished and it became more of an even matchup. The top two SCA leading players were the notable Lodeiro of Seattle and Pavon of LA. For a casual player who does not follow the MLS, the SCA metric can go a long way in pointing out who is deserving of the Captain spot, regardless of whether or not they end up taking sets. (Hint: the more sets you take, the more likely the SCA score is higher – this could lead you to figuring out who takes sets if you don’t have that info at your disposal).

How did these leading stat players do?

Lodeiro – 22.82
Pavon – 16.58

Given Seattle’s slight lean to win, Lodeiro definitely became the popular Captain pick which only backed up the SCA metric. Pavon was a close second and did not disappoint as well.

The remaining players that we were basically left with were almost in a bucket of similar proportions.

Ruidiaz – 16.44
Kljestan – 1.46
A. Roldan – 11.62
Gonzalez – 7.76
Lletget – 7.20
Svensson – 6.58

As illustrated, it was quite the lack of enthusiasm in these players. Ruidiaz only succeeded as he had scored but given he was the third highest ranked in SCAper90 it made sense to have him. Kljestan ranked fourth and although he did not score much, he only costed $3.2K which is close to bare minimum so that would have not hurt you much. The rest of the pack was pretty consistent with each other and produced as expected based on the SCA metric. Therefore, you can find solace in the fact that the SCA per 90 metric would have led to a strong showdown line.

Conclusion

There are tons of metrics out there to use to enforce your ability to gain edge over your competitors in DFS. This is just one of them, but this is one that I highly rate. Keep an eye out for more posts like this, and perhaps I will engage in a preview blog the day before a showdown slate to highlight players I think will fit well in a lineup. Be open to trying new things and stay ahead of the game!

Follow me on twitter @CarlitosWay_DFS.

About the Author

Comments

  • FitFantasyGod

    Wow man very interesting article! I love how you tie in how it’s relevant and actually show the data with it! Fantastic stuff my guy. If you play NFL showdown I just wrote an article on tonight’s showdown if you’re interested!

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