// Glicko-2 Rating calculator package. // // This package implements the Glicko-2 rating system algorithm written by // Professor Mark E. Glickman. All rating inputs and outputs are Glicko ratings, // but internally everything is converted to and considered Glicko-2 ratings. // // Glicko-2 is an improvement on the original Glicko, which was, in turn, an // improvement on the ELO system. // // The Glicko-2 system is specified on http://www.glicko.com/ // package glicko2 import ( "fmt" "math" ) const dvolatility float64 = 0.3 // Glicko2 state reprensentation (Glicko2 uses different scales from Glicko) type Glicko2 struct { rating float64 deviation float64 volatility float64 wins [](*Glicko2) losses [](*Glicko2) draws [](*Glicko2) } // Initializer with rating, rating deviation, and volatility specified. func NewGlicko2(rating float64, deviation float64, volatility float64) *Glicko2 { return newGlicko2(rating, deviation, volatility) } // Initializer with default rating, rating deviation, and volatility. func NewDefaultGlicko2() *Glicko2 { return newGlicko2(1500.0, 350.0, 0.06) } // helper for initialization func newGlicko2(rating float64, deviation float64, volatility float64) *Glicko2 { g := new(Glicko2) g.SetRating(rating) g.SetDeviation(deviation) g.SetVolatility(volatility) return g } // Get the current rating. This rating is a Glicko rating, not a Glicko2 rating. // For details, please see http://www.glicko.com/ func (g *Glicko2) Rating() float64 { return ((g.rating * 173.7178) + 1500.0) } // Set Glicko rating. Internally this is converted to a Glicko2 rating. // For details, please see http://www.glicko.com/ func (g *Glicko2) SetRating(rating float64) { g.rating = (rating - 1500.0) / 173.7178 } // Get the current rating deviation. This is a Glicko rating deviation, not a // Glicko-2 RD. For details, please see http://www.glicko.com/ func (g *Glicko2) Deviation() float64 { return (g.deviation * 173.7178) } // Set Glicko rating deviation. Internally this is converted to a Glicko-2 RD. // For details, please see http://www.glicko.com/ func (g *Glicko2) SetDeviation(deviation float64) { g.deviation = deviation / 173.7178 } // Get the current rating volatility. func (g *Glicko2) Volatility() float64 { return g.volatility } // Set rating volatility. func (g *Glicko2) SetVolatility(volatility float64) { g.volatility = volatility } // helper to make a copy of a Glicko2 struct func (g *Glicko2) duplicate() *Glicko2 { c := *g return &c } // Clear all results previously added via add_result(), add_win(), add_loss(), // and/or add_draw(). This method is called automatically whenever update() // is called. func (g *Glicko2) ClearResults() { g.wins = nil g.losses = nil g.draws = nil } // Add a win result to this rating. Note that no calculation is performed until // Update() is called. func (g *Glicko2) AddWin(loser *Glicko2) { g.wins = append(g.wins, loser.duplicate()) } // Add a loss result to this rating. Note that no calculation is performed until // Update() is called. func (g *Glicko2) AddLoss(winner *Glicko2) { g.losses = append(g.losses, winner.duplicate()) } // Add a draw result to this rating. Note that no calculation is performed until // Update() is called. func (g *Glicko2) AddDraw(drawer *Glicko2) { g.draws = append(g.draws, drawer.duplicate()) } // Print a Glicko2 struct func (g *Glicko2) Print() { fmt.Printf(" rating: %v\n", g.Rating()) fmt.Printf(" deviation: %v\n", g.Deviation()) fmt.Printf("volatility: %v\n", g.Volatility()) for _, win := range g.wins { fmt.Printf(" *beat\n") fmt.Printf(" rating: %v\n", win.Rating()) fmt.Printf(" deviation: %v\n", win.Deviation()) fmt.Printf(" volatility: %v\n", win.Volatility()) } } // util func func calcG(deviation float64) float64 { return 1.0 / (math.Sqrt(1.0 + 3.0*(deviation*deviation)/(math.Pi*math.Pi))) } // util func func calcE(rating float64, rating_opponent float64, deviation_opponent float64) float64 { return 1.0 / (1.0 + math.Exp(-calcG(deviation_opponent)*(rating-rating_opponent))) } // Update rating based on current results list, and clear results. func (g *Glicko2) Update() { // merge wins, losses, draws slices for convenience results := append(append(g.wins, g.losses...), g.draws...) // bail if no opponents set if len(results) == 0 { g.deviation = math.Sqrt((g.deviation * g.deviation) + (g.volatility * g.volatility)) return } // compute variance variance := 0.0 for _, r := range results { g_i := calcG(r.deviation) e_i := calcE(g.rating, r.rating, r.deviation) variance += (g_i * g_i) * e_i * (1.0 - e_i) } variance = 1.0 / variance // compute delta delta := 0.0 for _, r := range g.wins { delta += calcG(r.deviation) * (1.0 - calcE(g.rating, r.rating, r.deviation)) } for _, r := range g.losses { delta += calcG(r.deviation) * (0.0 - calcE(g.rating, r.rating, r.deviation)) } for _, r := range g.draws { delta += calcG(r.deviation) * (0.5 - calcE(g.rating, r.rating, r.deviation)) } delta *= variance // determine new volatility new_volatility := 0.0 a := math.Log((g.volatility * g.volatility)) x := 0.0 x_new := a for math.Abs(x-x_new) > 0.0000001 { x = x_new d := (g.deviation * g.deviation) + variance + math.Exp(x) h1 := -(x-a)/(dvolatility*dvolatility) - 0.5*math.Exp(x)/d + 0.5*math.Exp(x)*(delta/d)*(delta/d) h2 := -1.0/(dvolatility*dvolatility) - 0.5*math.Exp(x)*((g.deviation*g.deviation)+variance)/(d*d) + 0.5*(delta*delta)*math.Exp(x)*((g.deviation*g.deviation)+variance-math.Exp(x))/(d*d*d) x_new = x - h1/h2 } new_volatility = math.Exp(x_new / 2.0) // update the rating deviation to the new pre-rating period value pre_deviation := math.Sqrt((g.deviation * g.deviation) + (new_volatility * new_volatility)) // update the rating and deviation new_deviation := 1.0 / (math.Sqrt(1.0/(pre_deviation * pre_deviation) + 1.0/variance)) new_rating := 0.0 for _, r := range g.wins { new_rating += calcG(r.deviation) * (1.0 - calcE(g.rating, r.rating, r.deviation)) } for _, r := range g.losses { new_rating += calcG(r.deviation) * (0.0 - calcE(g.rating, r.rating, r.deviation)) } for _, r := range g.draws { new_rating += calcG(r.deviation) * (0.5 - calcE(g.rating, r.rating, r.deviation)) } new_rating = new_rating * (new_deviation * new_deviation) new_rating += g.rating // wipe our result lists g.ClearResults() // copy new values g.deviation = new_deviation g.volatility = new_volatility g.rating = new_rating }