Player/Team Projections -- 5

I am new into this and am looking for somebody with experience with machine based learning taking into account numerous variables that impact results of a team or individual player to ultimately predict outcomes. Machine based learning with the model "Generalised linear model with elastic net regularisation" (caret name glmnet) from my research has been the best model I can locate. Please read "Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach" (attached) as this illustrates what I want to build to predict head to head matches and then apply this similar learning to accurately predict how many fantasy points a player will score (fantasy points are calculated including how many times a player kicks it, scores a goal etc that combine and accumulate for fantasy points). For the fantasy perspective if you search "Draftkings" or "Draftstars" this is where I would use the player based fantasy projection. Different factors included in affecting the result of a head to head game: - Venue, weather, strength of teams, ladder position (see figure 3: in attachment) Different factors that will affect players fantasy output: - past history at venue, past history v opposition, number of days rest, home or away, strength of team etc Any experience with such coding that has been applied on other sports (football, NBA etc.) please feel free to get in touch.