model{
  C <- 0
    for( i in 1:nGames ){
    # Min score
    minscore[i] <- min( Score1[i],Score2[i]) + 1
    # Generate minscore latent variable
    u[i] ~ dunif(0,minscore[i])
    z3[i] <- trunc( u[i] )
    # Calculate z1 and z1 latent variables
    z1[i] <- Score1[i] - z3[i]
    z2[i] <- Score2[i] - z3[i]
    Zeros[i] ~ dpois( zeros.mean[i] )
    zeros.mean[i] <- -l[i] + C
    l[i] <- -lambda[i,1] + z1[i]*log( lambda[i,1] ) - loggam( z1[i]+1 )
            -lambda[i,2] + z2[i]*log( lambda[i,2] ) - loggam( z2[i]+1 )
            -lambda[i,3] + z3[i]*log( lambda[i,3] ) - loggam( z3[i]+1 )

    log( lambda[i,1] ) <- mu + offense[Team1[i]] + defense[Team2[i]]
    log( lambda[i,2] ) <- mu + offense[Team2[i]] + defense[Team1[i]]
    log( lambda[i,3] ) <- beta
  }

  for( j in 2:nTeams ){
    offense[j] ~ dnorm(0,.0001)
    defense[j] ~ dnorm(0,.0001)
  }
  offense[1] <- -sum( offense[2:nTeams] )
  defense[1] <- -sum( defense[2:nTeams] )
  mu~dnorm(0,0.001)
  beta~dnorm(0,.001)
}