Sunday, February 17, 2019

Overwatch League Fantasy: Should you start players in sweeps?

In fantasy football, team match-ups play a huge role in deciding when to start and bench players - fantasy owners usually starting wide-receivers against the Jets (who allowed an average of 28.5 fantasy points per game to WR in 2018), but tended to sit anyone who wasn't a superstar against the Jaguars (who allowed just 16.8 points per game to WR in 2018, best in the NFL). Overwatch Fantasy is little different - fantasy owners should pay close attention to who teams are playing against - team strength can play a huge role in your fantasy points.

Should you start a player even when you think their team will get curb-stomped? How about when you think your team will do the curb-stomping? What about betting on a crucial fifth map, where players can boost their play time with some extra minutes? Here's your guide to figuring out who/when to start in the Overwatch League based on how you think team units will play.

A brief note on terminology
  • The word "Map" is used to refer to a single discrete instance of team competition within an Overwatch League game, or "Match". An example of a map might be Busan, Dorado, or Route 66.
  • The word "Match" is used to refer to multiple maps which are played in a game. Teams generally play four maps in a single match but may play a fifth map should the teams be tied after playing four maps.

Part One: Establishing Baselines

To determine when you should be starting and sitting players in matchups, we'll first need to establish baselines for fantasy play. We'll use OWL Stats from 2018's regular season with HighNoon.GG's fantasy scoring system. It is of note that these stats represent fantasy stats from a 2/2/2 meta as opposed to a GOATS meta, but much of the same principles hold true as we are generally looking at the view from 20,000 feet as opposed to breaking these stats down by hero-choice or role.

We'll first calculate the average number of fantasy points accumulated per game. In Overwatch League stage play in 2018, the league recorded collectively the following values:

Total Damage      Total Elims   Total Heals
130,285,657.71212,44048,777,512.61

HighNoon.GG's scoring system uses the following scoring system based off of these metrics:
  • 1 point per 1,000 damage recorded
  • 1 point per 1,000 healing recorded
  • 0.5 points per elimination recorded
The breakdown of fantasy points recorded leaguewide in each category is as follows:

Damage Fantasy Points    Eliminations Fantasy Points      Healing Fantasy Points
130285.66106220.0048777.51

Thus, a total of 285,283.17 fantasy points were recorded by OWL players in 2018. The Overwatch League had 250 matches during stage play and a total of 12 players were fielded by both teams at any given time - thus, the average fantasy points per player-slot per match was 95.09 fantasy points. Thus, 95.09 is our total baseline for comparison. The breakdown of average points per category per match per player-slot is as follows:

Damage Fantasy Points     Eliminations Fantasy Points      Healing Fantasy Points
43.4335.4116.26

Part Two: Winners and Losers

One of the considerations in terms of starting/sitting might be who is expected to win and lose a game. In general, winning teams record an average of 101.76 fantasy points per match per role slot, and losing teams recorded 88.42 fantasy points per match per role slot. This revelation is patently obvious - fantasy points generally measure positive objectives, and a team will reach these objectives frequently en route to winning.

However, it is of note that this differential comes almost entirely from eliminations.
Damage Fantasy Points    Eliminations Fantasy Points   Healing Fantasy Points
Average43.4335.4116.26
Losing Teams42.0430.1916.19
Winning Teams44.8240.6216.33

There is almost a ten-point spread in eliminations between winning and losing teams, but only a two-point spread in damage and less than 0.2-point spread in healing. This result suggests that main-supports whose value largely comes from healing, such as Unkoe, Gido, and Revenge, do not necessarily need to have the outcome of the match factored into the decision to start or sit those players.

A quick check of last year's fantasy point totals confirms this. The following players had the smallest differential in fantasy point totals between wins and losses among players with at least 300 minutes played in both wins and losses, with % of Points from healing representing that player's rate stat across both wins and losses:
Player   Points/Game in Wins   Points/Game in Losses   Differential   % of Points from Healing
Closer47.0055.33-8.3377.2%
Mistakes89.5695.13-5.565.1%
Bani45.0950.63-5.5391.1%
sinatraa81.2785.40-4.134.6%
Libero89.9293.75-3.831.4%
Kellex66.3767.53-1.1682.2%
Hydration79.0080.13-1.135.2%
Moth62.0862.080.0081.9%
Gesture86.6486.120.520.2%
Coolmatt89.7789.160.610.1%

And the following players had the largest differentials:
Player   Points/Game in Wins   Points/Game in Losses   Differential   % of Points from Eliminations
ShaDowBurn110.0073.7836.2244.5%
Eqo112.1979.3332.8540.4%
Asher79.9347.4032.5348.7%
Seagull115.9085.6730.2339.1%
Agilities100.6073.6226.9837.9%
Envy130.09103.3326.7647.8%
Carpe112.8688.2524.6147.1%
NotE117.2392.8324.4044.5%
Boombox132.54108.6923.8525.8%
FLETA105.0581.5023.5541.3%

Again, it is not unreasonable to expect players to perform poorly against better opponents, but this information confirms that it is more difficult for players - especially elim-heavy fantasy players - to rack up more fantasy points in losses than wins.

This information indicates that expected match outcome is a non-factor in determining whether or not to start a main-support player, yet it may be worthwhile to bench a DPS player who relies on eliminations for points should you anticipate that they may be walking into a potential loss for an inferior DPS player playing for a team who expects to win.

Part Three: Expected Map Differential and Fantasy Points

There are a number of different lines of thinking in starting fantasy players when taking into team strength into consideration. We will define three different rationalizations, and then objectively examine them. Please do not read too much into my characterizations of each team - the point is not how I evaluate each team, rather, they are names ascribed to examples of teams of fictional strength.

Example A: Curb Stomping
A fantasy owner owns Meko, a player for the notable powerhouse NYXL. NYXL's only match this week is against the Florida Mayhem, a fairly poor team that NYXL is expected to sweep with ease. The fantasy manager starts Meko on the grounds that Meko will pick up many points in an easy victory over an inferior Mayhem team. However, should this manager consider that these games may be over more quickly, thus robbing Meko of the chance to pick up more fantasy points?

Example B: Getting Curb-Stomped
A fantasy owner owns Geguri, a player for the fairly weak Shanghai Dragons. The Dragons' only match this week is against the Philadelphia Fusion, the runner-ups from the Overwatch League championships in 2018 and a very strong team this season, and are overwhelmingly favored. The fantasy manager starts Geguri on the basis that she is an excellent flex-tank. However, will Geguri's production struggle given that she is playing against a superior team and that, in a sweep, the games may be over more quickly?

Example C: The Even Match
A fantasy owner owns Shadowburn, a player for the middle-of-the-road Paris Eternal. The Eternal play the Atlanta Reign in their only match this week, and it is expected to be a close and tight game. Despite the fact that Shadowburn may be receiving a healthy degree of competition, the owner starts Shadowburn on the basis that the games will be long and drawn out, thus giving Shadowburn more time to accumulate fantasy points.

Which of these lines of thinking are logical? Let's examine how many fantasy points players in different map spreads tend to receive.

There are a number of map-differentials that teams might encounter. A clean sweep represents a 4-0, a somewhat closer match would result in a 3-1 win, and a tied match after four maps means that one team will be walking away with a 3-2 win. There is also the possibility for draws, meaning that 3-0 and 2-1 outcomes are possible.

From 2018, here are the average points per match per player slot for teams in these different situations:

Map Differential   Outcome   Points/Game
3 to 2Winner115.30
2 to 1Winner113.13
3 to 2Loser109.09
3 to 0Winner106.53
2 to 1Loser101.76
3 to 1Winner95.07
4 to 0Winner94.57
3 to 0Loser93.92
3 to 1Loser83.56
4 to 0Loser72.99
In general, 3-2 matches tend to be the most productive in terms of fantasy outcomes for both teams - indicating that scenario C represents the greatest potential for fantasy points in a vacuum. It is also apparent that 3-2 wins and losses are dragging the averages for wins and losses overall upwards.

Starting a player in a game where the team is expected to win 3-1 or 4-0 represents an average fantasy opportunity, with both values appreciably close to the overall average for fantasy points per game per player slot. However, starting a player in a game that they might be expected to lose 4-0 means that they might stand to finish in excess of twenty points below average - a significant handicap.

However, evenly matched games present the greatest fantasy opportunity - a match which goes to a fifth map represents an opportunity for about fifteen additional points for players on both sides. These kinds of match-ups should be targeted - given two players of identical caliber, the correct play is to start the player in the match that would be more evenly matched.

What about the risk of losing a 3-1? The 3-1 loss column likely exaggerates the actual difference in terms of the fantasy penalty for a team losing 3-1 to an evenly matched opponent, as the 3-1 loss category includes many more teams who faced off against a much stronger opponent yet managed to take a map off of them, but even if we assume the penalty to be consistent across all teams as a worst-case scenario, the expected value in terms of fantasy points relative to the league average is as follows for a game between two evenly matched teams (such that P(Team A wins) = P(Team B wins) = 0.50):

Team A loses 0-4   Team A loses 3-1   Team A loses 3-2   Team A wins 3-2   Team A wins 3-1   Team A wins 4-0   Expected Value
Odds of Map Differential6%25%19%19%25%6%100%
Fantasy Points Above Average-22.10-11.5314.0020.21-0.20-0.52N/A
Expected Value-1.38-2.882.633.79-0.05-0.032.07

By a back-of-the-napkin calculation, it certainly appears as though starting players in evenly matched games is worth the risk of the 3-1 or 4-0 as our expected gain in terms of average points is positive (+2.07).

Why might players stand to gain so much from playing in close 3-2 matches? It certainly appears to be match-time. 3-2 matches do, by virtue of that fifth map, record significantly more play-time than other match differentials.
Map Differential   Average Match Length (Min)
3 to 263.63
2 to 158.13
3 to 055.92
3 to 149.93
4 to 047.33
However, we should not discount the possibility of the strength of competition driving point totals as well. In terms of rate stats, it appears as though evenly matched teams post average point differentials against each other, whereas teams curb-stomping opponents generate a high degree of points-per-ten minutes (league average of 17.79 points per 10 minutes).
Map Differential   Outcome   Fantasy Points Per Slot Per 10 Min
4 to 0Winner20.03
2 to 1Winner19.47
3 to 0Winner19.05
3 to 1Winner19.04
3 to 2Winner18.12
2 to 1Loser17.51
3 to 2Loser17.15
3 to 0Loser16.80
3 to 1Loser16.76
4 to 0Loser15.42
Yet, as demonstrated above, the difference in rates of accumulation does not compensate for the brevity of four-map games.

It is also of note that games with drawn-maps also tend to display longer times and similar points-per-ten - indicating that these games are quite close as well. However, map draws are fairly rare and unpredictable enough that I have felt comfortable not including them in the larger discussion of this analysis.

Conclusion: Notes on Synergy and Impact

There is a natural question of, "The chicken or the egg": do fantasy teams truly post high totals by virtue of winning, or do they simply accumulate these high totals en route to winning, and we are mistaking the disease for the symptom? It is obvious that evenly matched teams present an opportunity for additional points by virtue of the fifth map, yet teams who win tend to simply have better players overall, and this is what is ultimately measured by fantasy points, not simply wins.

The answer is, it is probably both the chicken and the egg. In baseball, it is rather easy for a good player to have an excellent performance in a losing effort - like Mike Trout going 2-3 with two doubles and a walk in an 8-2 loss - but it is more difficult to accomplish that feat in Overwatch, especially in a GOATs meta where getting the first kill tends to result in the rest of the team dying or running away. It is obvious that winning is a function of player skill, yet it is also the function of multiple players' skills, and the other players on the team ultimately affect each others' fantasy point totals. Janus was not an awful fantasy player with NYXL, but watching him falter against his former teammates while playing on a much weaker team on Saturday was a reminder that team strength plays an important role in fantasy, as does the quality of opponent. Both of these factors are factored into the discussion of winning/losing and map-differential.

In that respect, consider these statistics to be overstated, but only to a degree. Yes, good fantasy players play for good teams, and teams tend to put up more points in fantasy wins. But at the same time, expectations regarding winning/losing can help predict fantasy stats. And by recognizing the potential for a 3-2 match, you might pick up some bonus points with ease.

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