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KNVB Beker Women stats & predictions

Overview of Tomorrow's KNVB Beker Women Matches

The excitement is palpable as fans of women's football in the Netherlands eagerly await the upcoming matches in the KNVB Beker. With several teams vying for supremacy, the tournament promises thrilling encounters and showcases some of the finest talents in Dutch women's football. This guide will provide expert insights into tomorrow's matches, offering betting predictions and detailed analyses to enhance your viewing experience.

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Key Matches to Watch

  • AFC Ajax vs. SC Heerenveen: Known for their tactical prowess, AFC Ajax will face off against a resilient SC Heerenveen. This match is expected to be a tactical battle with both teams looking to capitalize on set pieces.
  • PSV Eindhoven vs. FC Twente: A clash between two top contenders, this match promises high stakes as both teams aim to assert their dominance early in the competition.
  • FC Utrecht vs. AZ Alkmaar: With a history of intense rivalry, this fixture is anticipated to be a closely contested affair, with both sides eager to prove their mettle.

Betting Predictions and Insights

Betting enthusiasts have been closely analyzing team performances, player form, and head-to-head statistics to provide informed predictions for tomorrow's matches.

AFC Ajax vs. SC Heerenveen

Analysts predict a narrow victory for AFC Ajax, citing their recent form and home advantage. Betting odds favor Ajax at 1.8, with a draw at 3.5 and Heerenveen at 4.0.

PSV Eindhoven vs. FC Twente

PSV Eindhoven is favored to win with odds at 2.0, while FC Twente trails at 3.3. The possibility of an over/under goal market suggests a high-scoring game, with over 2.5 goals at odds of 1.9.

FC Utrecht vs. AZ Alkmaar

This match is expected to be tightly contested, with AZ Alkmaar slightly favored at odds of 2.1 compared to Utrecht's 3.2. A draw is considered a strong possibility at odds of 3.4.

Expert Analysis on Team Form

AFC Ajax

Under the guidance of their experienced coach, AFC Ajax has shown remarkable consistency in their recent matches. Their attacking strategy, characterized by quick transitions and precise passing, has been instrumental in their success.

SC Heerenveen

SC Heerenveen has been focusing on strengthening their defense while maintaining an aggressive attacking approach. Their ability to counter-attack effectively makes them a formidable opponent.

PSV Eindhoven

PSV Eindhoven boasts one of the most potent attacking line-ups in the league, with key players delivering consistently high performances. Their offensive prowess is expected to be a decisive factor in tomorrow's match.

FC Twente

FC Twente has been working on improving their midfield control and defensive solidity. Their recent matches have shown improvements in these areas, which could play a crucial role against PSV Eindhoven.

FC Utrecht

Known for their disciplined play and strategic approach, FC Utrecht has been focusing on building from the back and utilizing quick wingers to break down defenses.

AZ Alkmaar

AZ Alkmaar's balanced squad and tactical flexibility make them a challenging opponent. Their ability to adapt during matches often gives them an edge over rivals.

Tactical Breakdowns

AFC Ajax vs. SC Heerenveen: Tactical Insights

  • Ajax's Formation: Likely to deploy a fluid attacking setup that allows for dynamic movement and positional interchange among forwards.
  • Heerenveen's Strategy: Expected to focus on defensive solidity while looking for opportunities to exploit any gaps left by Ajax's high pressing game.
  • Potential Matchups: Key battles will include Ajax's creative midfielders against Heerenveen's defensive midfielders.
  • Critical Factors: Possession retention and effective use of wing play will be crucial for Ajax.

PSV Eindhoven vs. FC Twente: Tactical Insights

  • PSV's Approach: Anticipated to dominate possession with quick passing sequences aimed at breaking down Twente's defense.
  • Twente's Game Plan: Likely to employ a compact defensive structure while looking to counter-attack through fast breaks.
  • Potential Matchups: PSV's forwards against Twente's central defenders will be pivotal.
  • Critical Factors: PSV's ability to control the midfield and Twente's effectiveness in transitioning from defense to attack.

FC Utrecht vs. AZ Alkmaar: Tactical Insights

  • Utrecht's Strategy: Expected to focus on maintaining shape and exploiting spaces through wide play.
  • AZ's Tactics: Likely to utilize a flexible formation that adapts based on the flow of the game.
  • Potential Matchups: Utrecht's wingers against AZ's fullbacks will be critical in determining wide play effectiveness.
  • Critical Factors: Utrecht's ability to disrupt AZ's rhythm and AZ's capacity to exploit any defensive lapses by Utrecht.

Betting Tips and Strategies

  • AFC Ajax vs. SC Heerenveen: Consider betting on Ajax to win with both teams scoring (BTTS) given their attacking tendencies.
  • PSV Eindhoven vs. FC Twente: A bet on over/under goals might be lucrative due to PSV's offensive strength.
  • FC Utrecht vs. AZ Alkmaar: A draw could be a wise choice given the balanced nature of both teams.
  • Mixed Parlays: Combining bets on different outcomes across matches could maximize potential returns.
  • Total Goals Market: Given the attacking prowess of several teams, betting on higher total goals could be rewarding.
<|repo_name|>KumagaiTakumi/Simulation-of-Customer-Satisfaction<|file_sepuser#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D import pandas as pd class customer_satisfaction(): def __init__(self): self.N = int(input('人数を入力してくださいn')) self.d = int(input('時間を入力してくださいn')) self.A = int(input('受付席数を入力してくださいn')) self.B = int(input('調査席数を入力してくださいn')) self.C = int(input('手続き席数を入力してくださいn')) self.p1 = float(input('受付での待ち時間に対する不満度を0~1で入力してくださいn')) self.p2 = float(input('調査での待ち時間に対する不満度を0~1で入力してくださいn')) self.p3 = float(input('手続きでの待ち時間に対する不満度を0~1で入力してくださいn')) self.r1 = float(input('受付の終了後、次の作業に進む確率を0~1で入力してくださいn')) self.r2 = float(input('調査の終了後、次の作業に進む確率を0~1で入力してくださいn')) self.r4 = float(input('手続きの終了後、次の作業に進む確率を0~1で入力してくださいn')) self.v1 = np.random.uniform(0,5,size=self.N) self.v2 = np.random.uniform(0,5,size=self.N) self.v4 = np.random.uniform(0,5,size=self.N) def calc_satis(self): self.satis_list = [] for i in range(self.N): satis_i = self.calc_satis_one(i) self.satis_list.append(satis_i) def calc_satis_one(self,i): wait1_queue_time,self.wait1_time,self.wait1_satis = self.calc_wait_time(self.A,self.p1,self.v1[i],self.d) wait2_queue_time,self.wait2_time,self.wait2_satis = self.calc_wait_time(self.B,self.p2,self.v2[i],self.d) wait4_queue_time,self.wait4_time,self.wait4_satis = self.calc_wait_time(self.C,self.p3,self.v4[i],self.d) if wait1_queue_time == -9999: return -9999 if wait1_queue_time != -9999: if self.r1 == -9999: return -9999 if wait2_queue_time == -9999: return -9999 if wait4_queue_time == -9999: return -9999 if self.r4 == -9999: return -9999 if self.r2 == -9999: return -9999 satis_one = (self.r1*wait1_satis + (1-self.r1)*wait2_satis + (self.r1*self.r2*wait4_satis))/((self.r1*self.r2*self.r4)) return satis_one def calc_wait_time(self,A,p,v,d): time_list = [0]*A time_list[0] += v for t in range(d): for j in range(A): time_list[j] += v if time_list[j] >= d: time_list[j] -= d break for j in range(A): time_list[j] += p if time_list[j] >= d: time_list[j] -= d break queue_time_list = [] for i in range(A): queue_time_list.append(time_list[i]-v) wait_queue_time_list = queue_time_list[queue_time_list > v] if len(wait_queue_time_list) == A or len(wait_queue_time_list) == A-1: wait_queue_time_mean = np.mean(wait_queue_time_list) wait_queue_time_stddeviation = np.std(wait_queue_time_list) wait_queue_time_mean -= v wait_queue_time_stddeviation /= (A-1) wait_queue_time_mean *= p/(1-p) if wait_queue_time_mean > d: return -9999,-9999,-9999 wait_queue_satis = p**(wait_queue_time_mean/v) return wait_queue_mean,d*wait_queue_satis,v*wait_queue_satis def calc_probability(self,A,p,v,d): def main(): cust_sat=customer_satisfaction() cust_sat.calc_satis() print(cust_sat.satis_list) if __name__ == '__main__': main()<|file_sep<|file_sep|># Simulation-of-Customer-Satisfaction<|repo_name|>KumagaiTakumi/Simulation-of-Customer-Satisfaction<|file_sep officially begin begin new line end delete begin new line end delete begin new line end delete begin new line end import numpy as np import matplotlib.pyplot as plt #設定値の取得 # N人が順番に来る N=int(input("人数を入力して下さい。")) d=int(input("営業時間を入力して下さい。")) #各ステーションについて A=int(input("受付窓口数を入力して下さい。")) B=int(input("調査窓口数を入力して下さい。")) C=int(input("手続き窓口数を入力して下さい。")) #待ち時間への不満度パラメーター pa=float(input("受付での待ち時間に対する不満度パラメーターを0~1で入力して下さい。")) pb=float(input("調査での待ち時間に対する不満度パラメーターを0~1で入力して下さい。")) pc=float(input("手続きでの待ち時間に対する不満度パラメーターを0~1で入力して下さい。")) #次の作業へ進む確率パラメーター ra=float(input("受付が終わった後、次の作業へ進む確率パラメーターを0~1で入力して下さい。")) rb=float(input("調査が終わった後、次の作業へ進む確率パラメーターを0~1で入力して下さい。")) rc=float(input("手続きが終わった後、次の作業へ進む確率パラメーターを0~1で入力して下さい。")) #人ごとに異なるサービス所要時間 vi=np.random.uniform(0,5,size=N) vj=np.random.uniform(0,5,size=N) vk=np.random.uniform(0,5,size=N) #一人あたりの満足度計算関数 def sats(i): wa=[] wb=[] wc=[] for j in range(A): wa.append(0) for k in range(B): wb.append(0) for l in range(C): wc.append(0) via=vi[i] vjb=vj[i] vkc=vk[i] wa[0]+=via for t in range(d): for j in range(A): wa[j]+=via if wa[j]>=d: wa[j]-=d break for j in range(A): wa[j]+=pa if wa[j]>=d: wa[j]-=d break wb[0]+=vjb for t in range(d): for k in range(B): wb[k]+=vjb if wb[k]>=d: wb[k]-=d break for k in range(B): wb[k]+=pb if wb[k]>=d: wb[k]-=d break wc[0]+=vkc for t in range(d): for l in range(C): wc[l]+=vkc if wc[l]>=d: wc[l]-=d break for l in range(C): wc[l]+=pc if wc[l]>=d: wc[l]-=d break qa=[wa[j]-via for j in range(A)] qb=[wb[k]-vjb for k in range(B)] qc=[wc[l]-vkc for l in range(C)] wq_a=[qa[j] for j in range(A) if qa[j]>via] wq_b=[qb[k] for k in range(B) if qb[k]>vjb] wq_c=[qc[l] for l in range(C) if qc[l]>vkc] if len(wq_a)==A or len(wq_a)==A-1: wq_a_mean=np.mean(wq_a) wq_a_stddeviation=np.std(wq_a) wq_a_mean-=via wq_a_stddeviation/= (A-1) wq_a_mean*=pa/(1-pa) if wq_a_mean>d: print('Error') break wq_a_sats=pa**(wq_a_mean/via) else: return -9999 if len(wq_b)==B or len(wq_b)==B-1: wq_b_mean=np.mean(wq_b) wq_b_stddeviation=np.std(wq_b) wq_b_mean-=vjb wq_b_stddeviation/= (B-1) wq_b_mean*=pb/(1-pb) if wq_b_mean>d: print('Error') break wq_b_sats=pb**(wq_b_mean/vjb) else: return -9999 if len(wq_c)==C or len(wq_c)==C-1: wq_c_mean=np.mean(wq_c) wq_c_stddeviation=np.std(wq_c) wq_c_mean-=vkc wq_c_stddeviation/= (C-1) wq_c_mean*=pc/(1-pc)