1718t1is428T14/Proposal

Formula One, better known as F1, is the highest class of racing for open-wheel automobiles that is sanctioned by the Fédération Internationale de l'Automobile (FIA),the governing body of motor sports. Open-wheel cars are cars with wheels outside of the main body, of which F1 cars are the most well-known. With their powerful engines and lightweight, aerodynamic bodies, F1 cars are capable of speeds of up to 375 km/h and are one of the fastest in the world in terms of acceleration, cornering, and overall lap times around a road circuit.

Held annually since 1950, the FIA Formula One World Championship is a race series that garners a massive base of fans and spectators, and has become one of the most coveted automotive events and spectator sports of all time. Each F1 championship, known as a "season", consist of approximately 20 races, each known as a "Grand Prix", held in circuits in different countries all over the world. The results of each race are tabulated in a points-based system to determine two annual Championship winners, one among drivers and one among constructors(teams).

Our project aims to provide insights to F1 drivers and teams by generating comprehensive visual data representations, segmented by the various factors that affect victory in an F1 Grand Prix (based on historical data) to help teams generate more effective strategies to increase their likelihood of victory.

Our project will be using the official dataset from Overwatch, available on [....] as well as specialized Overwatch tracking site such as Overbuff.com and Master Overwatch. As raw data downloading options are currently unavailable, our group has chosen to transcribe the data to our own Excel spreadsheet.

Our project will analyze the following attributes from the dataset:

• • Hero
  • Pick rate
  • Win rate
  • Tie rate
  • On fire
  • Elims
  • Obj. Kills
  • Obj Time
  • Damage

Additionally, our project will also use another dataset for the combination of different heroes’ ultimates. In particular, this will be a matrix that details the effectiveness of a hero’s ultimate when used together with another hero’s ultimate. Effectiveness is measured with the following ranges of value:

Bad: counterproductive when 2 ultimates are played together Ineffective: ultimates that are of similar types or types that do not combine well Maybe: ultimates can combine to but not the best A bit: ultimates combine to give medium effects Defense: ultimates combine to give good effects Nice: ultimates combine efficiently to give very good effects

Our approach to this problem is to first define a competitive player’s journey through a 2.5-month-long competitive season of Overwatch, then provide insights in each step of this journey to ensure the best possible performance. As the season begins, the player will participate in 10 ‘Placement Matches’ (PM). The player’s performance in these matches will determine his/her ‘Skill Ratings’ (SR), a value to denote player standings. Players are placed in different ‘Tiers’ based on SR as shown below (e.g. A player with SR between 1-1499 is placed in Bronze tier). Each post-PM competitive match thereafter causes the player’s SR to increase with each victory, or decrease with each loss, and the player’s Tier adjusts accordingly.

It is especially important for a player to perform well in the PM because this is the best opportunity for a player to quickly obtain high SR, and therefore high standings. Post-PM victories contribute comparatively small amounts to a player’s SR, unless many victories are obtained, but this takes much more time. The exact Overwatch algorithm for SR calculation during PM is unknown, but it is speculated to be based on the player’s individual performance during the PM (as opposed to team performance). Some players lose all 10 matches but still receive a high Tier, such as Grandmaster, due to high individual performance. In this project, it is therefore assumed that only individual performance affects the SR a player obtains from the PM.

This project will be separated into two sections. The first section will focus on the PM, To maximize a player’s individual performance in the PM, we will advise the most suitable heroes to select based on a player’s best skillsets and preferred playstyle.

The second section of the project will focus on post-PM matches. In these matches, there are several factors affecting the SR gained, but the most dominant factor by far is whether the player’s team wins or loses. Therefore, maximizing team performance will be prioritized over individual performance. We have identified four main aspects that affect team performance, namely the player’s team composition, map-based strategies, individual heroes’ ultimate abilities, and the enemy team’s composition. Through analysis of data regarding these factors, we will maximize the chances of team victory by providing strategies highlighting the most effective hero combinations for the team.