Next, we'll look at some data for a specific matchup of two professional teams Fnatic and Virtus. All data was gathered from hltv. For , we retrieved a total of 4, matches from online, lan and majors category. For Fnatic versus Virtus. Pro, we have a total of 35 matches on record. One of the things that my friend mentioned that made the betting market inefficient are people betting on the basis of taste rather than cold hard evidence.
Many people love to pick him due to his humour and personality, while in reality, he is easily the worst player on the team as ranked by our KDA score measure. For example, see below the player KDA distribution plot of all Virtus. While the former tends to yield few data points, the latter is an important distinction to consider which will be the primary focus within our analysis and modelling.
Let's give an example match for Fnatic vs. Like all models, we need some assumptions to simplify the process. We assume that each match result for our portfolio is independent of other matches - an assumption unlikely to be true but good enough for now. Let be the allocation vector such that be the decision to add the th player to the portfolio and be the associated salary for that th player. The set of all possible allocation is then equal to. It is interesting to look more into the portfolio variance components which is arithmetically composed of both individual player KDA variances as well as either pair of players' covariance.
The structure of each individual element is condensed into the covariance matrix mentioned above with it's diagonals being each player's KDA variance and off-diagonals being the covariance of KDA between player and. Normalizing into a correlation matrix values lie between 0 and 1 , we can visualize the score dependency of any two pair of players. Series names. The correlation heatmap effectively captures the dependency structure of KDA between players. Similarly, Snax and NEO tend to have a strong synergy where if one does well, the other tends to perform well also.
In most cases, the optimizer would select the players with the highest mean KDA. However, that method doesn't take into account the correlation between kills and thus, the resulting variance of the portfolio KDA. Therefore, the idea of placing conflicting bets can be a potentially powerful tool to hedge and increase portfolio consistency. As shown, the mixed team has a much higher consistency at the sacrifice of a lower mean KDA. Given all , there exists an optimal mix of allocated players that delivers maximum KDAC.
A list of recent matches this team has played, green indicates a win and red a loss. Top Rankings Monthly Competition Become a tipster! You can get involved too, just use the bet tracker on the left to make your prediction for this match. Bet Betway Betspawn Buff. Tips Leave your own tip! Live Odds. Recent Form A list of recent matches this team has played, green indicates a win and red a loss. Tipsters All Community Tipsters No ranked community predictions found yet, this will refresh every 30 seconds.
Indeed, if we assume each match is independent of each other, then the total portfolio score for the entire lineup follows a normal distribution such that. Where and are the portfolio mean KDA and consistency for the th match. DataFrame fset. In this chart, all blue dots are all with their estimated consistency on the x-axis and expected total portfolio KDA on the y-axis. Series [gkda, np. The cyan dot is the max KDAC within all valid s and the code is contained within the plotting code block, which is simply a search for max KDAC in fset.
In traditional MPT, this dot is said to be the point that is tangential to the optimal frontier red-line , which in our case, can't be analytically derived from a trivial equation. DataFrame np. Series [float x , fset[ fset.
I'm currently still forward testing this methodology and the results are somewhat mixed. Firstly, well done on making portfolio optimization considerably less unrelatable; I am definitely going to share this with all my gamer friends. Secondly, I wouldn't mind getting a copy of the data because I'd like to try some alternative optimization techniques. Please could you share it with me? Thanks in advance and well done again, keep up the solid work!
I can help with data gathering, I am a csgo bettor but instead of alphadraft I use csgolounge which is similar to sites like bwin fanobets is the most similar to bwin. I am interested in your data set! I am a beginner programmer and it has been really difficult for me to put the data in a usable form. If it would be possible, I would like to be able to use the data you have gathered. Thanks, Matt. Community Prediction Recommended team:. Stake: 0. Locked You must be logged in to place tips.
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