Metrics That Matter Most For Predicting LoL Matches

When predicting League of Legends Match outcomes, it’s necessary to do an in-depth analysis on performance metrics to identify team capabilities beyond just using win/loss record. Most observers will use the most recent results or current reputation of a team as the basis for their prediction, however, using only objective statistics will help identify patterns in performance and behavior over time.

Metrics that can be considered causal indicators (metrics indicating long-term success) are what should be used when predicting because they are likely to have a higher degree of accuracy in predicting future performance than metrics simply showing prior results. Data collected from matches has been shown by research studies as producing models with very strong predictive capabilities using significant amounts of historical data input to provide quality predictors.

-Does this statistic have any connection to winning or losing?
-Does this statistic provide valid evidence that a team has similar capabilities in past matches?
-Are current statistics similar to those produced/included in the historical sample?
-What type of relationship does this statistic have with other statistics?

Control of Early Game Gold and How it Affects the Outcome of a Match
Your early game performance is essential in setting up an advantage and a match's future outcome. A team's gold difference at 10 Minutes and 15 Minutes helps illustrate how teams can consistently achieve an early lead by performing better through superior laning, jungle pathing, and objective prioritization.

To understand a team's competitive rank, you must also understand how the different skill ranking systems contribute to how teams create their rosters and have expectations of the skills of their players. The MMR rating system in League of Legends is used to determine how fairly players are matched, and how their hidden skill ratings compare to their actual talent level. This skill metric is something that we use in evaluating teams through analytical forecasts.

In much the same way that MMR rating accounts for an opponent’s ability level and consistency of performance, our predictive models will take an early advantage accumulation into account but also measure the type of opponent and their team's potential for improvement or growth as well.

Video Breakdown of LoL strategy

Please embed: https://www.youtube.com/watch?v=qadfVOp4lqM

In League of Legends, just because a team has a significant gold lead early on in the game does not mean they will win the game. For teams that are behind, they can come back from this disadvantage through superior team fighting and/or late-game macro play. Examining team performance when ahead or behind gives us an understanding of both team resilience and the win conditions associated with different types of team formations or play styles.
While the first blood statistic is important, it carries less weight than total early-game gold generation. For instance, teams that consistently build gold advantages between 1-2k through superior laning fundamentals will have a much stronger early-game; compared to teams that are able to do so by getting isolated kills.

Objective Control Rates and Priority
Controlling objectives on the map directly affects the pressure that the team puts on an opponent and how much gold they are able to earn. By looking at dragon control rates, we can establish how well the team works together to secure a timed objective. By looking at baron control, we can see how well the team fights as a group and how they secure high-value neutral objectives.
When it comes to herald control, it is extremely important in professional play. Teams are able to use herald to apply systematic tower pressure, which helps them destroy more towers and gain gold advantages. Thus, if a team is able to at least destroy their first tower, they will have an advantage over the opponent in terms of vision control due to having more map pressure, which shows commitment to structured macro play.

Vision Control as Well as Map Awareness
"Vision Score per minute is an indicator of the amount of team commitment to the information warfare. A high vision score typically denotes or means that systematic warding has allowed for the safe farming of lanes, set-up of objective locations, as well as available opportunities to pick off opponents.

Vision Denial - Removing enemy wards will reflect the proactive nature of map control. A team that is consistently able to obtain a high rate of vision denial creates an uncertainty in the ability of the enemy to engage, giving the team with a high rate of vision denial numerous opportunities to control how these engagements occur.

Champion Pool Depth and Adaptation to Meta
Teams with a deeper Champion Pool place pressure on their opponents to draft and ban champions they are not comfortable with. The number of unique champion picks throughout the different matches has consistently demonstrated whether teams are using flexible strategies or narrow comfort zones.

The win rate for power pick champions reflects the efficiency of teams executing their strategies. Teams with a strong win percentage are typically experienced and knowledgeable; teams with a low win percentage are likely implementing surface-level meta without full comprehension."

Betting Platforms Provide Predictive Market Intelligence
When evaluating how predictions affect decision-making in the real world, bettors are able to bet on League of Legends games at Thunderpick bet against the aggregated model of the rest of the market.

The odds reflect aggregated intelligence from statistics, expert judgment and the overall opinion of the public.

By assessing your probabilities vis-a-vis the market odds, you can identify inefficiencies and any necessary adjustments to your model.

Data is especially valuable around patch transitions, where previous trends are no longer reliable and analysts need quick responses to new realities.

Team Fight Effectiveness and Damage Distribution
The percentage of winning fights during team fights is a reflection of coordinated execution under stress.

Teams that frequently win the fights they initiate tend to exhibit superior position and target selection.

In damage distribution metrics, you can measure a team's reliance on one carry versus multiple threats (balancing damage output).

Teams that have balanced damage output are much more difficult to counter, while teams that rely on concentrated damage output will be more vulnerable to exploitation.

Kill participation percentages (how often a player participates in the kill) are particularly important for the support and jungle roles as these positions demonstrate the unity of the team and the level of coordinated play.

Resource Allocation & Gold Efficiency

Gold advantages is only valuable when used efficiently

Gold to damage ratio measure how effectively team covert resources into impact

CS per minute differentials reveals how farm is distributed across roles

Good teams can be separated from great teams by the efficiency of their use of gold.
The amount of gold per unit of damage dealt will show how effectively a team has used their resources for damage.
Additionally, the number of champions per minute will show how well a team has distributed their farm among champions in different roles.
Some teams will focus on a few hyper-carries and as such invest heavily in those champions, while other teams will have a more balanced economy for a more versatile team composition.
The distance between the jungle and other lanes will also impact each team’s ability to reinforce their strengths and recover from weakness in a lane.

Conclusion
When looking at league of legends match predictions, one needs to rely on more than just surface-level observations. Such metrics as early gold amounts, objective control, dominance in vision, flexibility in champion selecting, and effectiveness in team fights may all give more insight into how a team performs as opposed to merely identifying how a team performed.
After all, the best predictions will come from using predictive models that utilize those metrics in a dynamic way to account for changes in the meta, and are compared against actual results.
Success in making predictions will ultimately depend on understanding not just what has happened, but also why something happened, and if similar situations are likely to happen again.