Overview of Arnavutkoy Belediyespor
Arnavutkoy Belediyespor, based in the vibrant region of Arnavutköy, Turkey, competes in the local football league. Founded in 1974, the team is currently managed by Coach Mehmet Yılmaz. Known for its passionate fanbase and competitive spirit, Arnavutkoy Belediyespor aims to establish itself as a formidable force in Turkish football.
Team History and Achievements
Since its inception, Arnavutkoy Belediyespor has experienced various phases of success. The club has secured multiple league titles and cup victories, with notable seasons often highlighted by their strategic gameplay and resilient performances. Their history is marked by a series of commendable achievements that have cemented their reputation in Turkish football.
Current Squad and Key Players
The current squad boasts several key players who are pivotal to the team’s performance. Among them are forward Ahmet Çelik, known for his goal-scoring prowess, and midfielder Emre Yıldız, celebrated for his tactical acumen. The team’s defense is anchored by veteran defender Mustafa Demir, whose experience and leadership are invaluable.
Team Playing Style and Tactics
Arnavutkoy Belediyespor typically employs a 4-3-3 formation, focusing on dynamic attacking strategies while maintaining a solid defensive structure. Their playing style emphasizes quick transitions and high pressing, capitalizing on their players’ agility and stamina. However, they occasionally struggle with set-pieces due to lapses in concentration.
Interesting Facts and Unique Traits
The team is affectionately nicknamed “The Lions of Arnavutköy,” a testament to their fierce competitiveness. They have a dedicated fanbase that supports them through thick and thin. Rivalries with neighboring clubs add an extra layer of excitement to their matches, while traditions like pre-game rituals foster a strong sense of community among fans.
Lists & Rankings of Players & Stats
- Ahmet Çelik: Top scorer 🎰
- Emre Yıldız: Most assists 💡
- Mustafa Demir: Fewest goals conceded ✅
Comparisons with Other Teams
In comparison to other teams in the league, Arnavutkoy Belediyespor stands out for its balanced approach between offense and defense. While some teams focus heavily on attacking play, Arnavutkoy maintains a more holistic strategy that often proves advantageous in tight matches.
Case Studies or Notable Matches
A memorable match was their thrilling victory against Gaziantepspor last season, where they overturned a two-goal deficit to win 3-2. This game showcased their resilience and tactical flexibility under pressure.
| Statistic | Data |
|---|---|
| Last 5 Matches Form | W-W-L-W-W |
| Head-to-Head Record vs Gaziantepspor | 3 Wins – 1 Loss – 1 Draw |
| Odds for Next Match Win | +150 |
Tips & Recommendations for Betting Analysis
- Analyze recent form trends to gauge momentum.
- Closely watch key player performances for insights into potential match outcomes.
- Evaluate head-to-head records against upcoming opponents to predict competitive edges.
Frequently Asked Questions (FAQ)
What is Arnavutkoy Belediyespor’s current league position?
The team currently holds the 5th position in the league standings, reflecting their consistent performance throughout the season.
Who are the standout players this season?
Ahmet Çelik has been exceptional as the leading goal scorer, while Emre Yıldız continues to be instrumental with his assists.
What are some key statistics to consider when betting?
Paying attention to recent form, head-to-head records against opponents, and individual player stats can provide valuable insights for making informed betting decisions.
Betting Tips: Pros & Cons of Current Form or Performance (✅❌ Lists)
- Potential Advantages:
- Momentum from recent wins ✅
- Tactical flexibility 💡
- Potential Disadvantages:
- Inconsistency against top-tier teams ❌
- Vulnerability during set-pieces ❌
- Bet on Arnavutkoy Belediyespor now at Betwhale!</l[0]: # -*- coding: utf-8 -*-
[1]: import numpy as np
[2]: from numba import njit[3]: @njit(cache=True)
[4]: def _logistic(z):
[5]: return np.exp(z) / (1 + np.exp(z))[6]: @njit(cache=True)
[7]: def _dlogistic(z):
[8]: return np.exp(z) / ((1 + np.exp(z)) ** 2)[9]: @njit(cache=True)
[10]: def _ddlogistic(z):
[11]: return (np.exp(z) * (1 – np.exp(z))) / ((1 + np.exp(z)) ** 3)[12]: @njit(cache=True)
[13]: def _neg_log_likelihood(x_data,
[14]: y_data,
[15]: beta,
[16]: log=False):[17]: n_samples = len(x_data)
[18]: if log:
[19]: res = np.empty(n_samples)
[20]: for i in range(n_samples):
res[i] = (-y_data[i] * x_data[i].dot(beta) +
np.log(1 + np.exp(x_data[i].dot(beta))))if y_data[i] == 0:
z = x_data[i].dot(beta)
z = x_data[i].dot(beta)
z = x_data[i].dot(beta)
res[i] = -z
res[i] = -z
if not np.isfinite(res).all():
[
]
raise ValueError("fitted parameters contain non-finite values.")
[
]
raise ValueError("non-finite value encountered in likelihood computation.")
[
]
raise ValueError("fitted parameters contain non-finite values.")
[
]
raise ValueError("non-finite value encountered in likelihood computation.")
[
]
raise ValueError("fitted parameters contain non-finite values.")
[
]
raise ValueError("non-finite value encountered in likelihood computation.")
[
]
raise ValueError("fitted parameters contain non-finite values.")
[
]
raise ValueError("non-finite value encountered in likelihood computation.")
[
]
@njit(cache=True)
def _score(x_data,
y_data,
beta):n_features = len(beta)
n_samples = len(y_data)
result = np.zeros(n_features)
f_beta_dot_x = x_data.dot(beta)
p_beta_dot_x = _logistic(f_beta_dot_x)
one_minus_p_beta_dot_x = 1 – p_beta_dot_x
dlog_p_beta_dot_x = _dlogistic(f_beta_dot_x)
w_i_arr = y_data * dlog_p_beta_dot_x / p_beta_dot_x –
(1 – y_data) * dlog_p_beta_dot_x / one_minus_p_beta_dot_xresult += x_data.T.dot(w_i_arr)
return result
n_features = len(beta)
n_samples = len(y_data)
result_0_rows_cols_shape_arr_0_arr_0_shape_0_rows_cols_shape_arr_0_shape_0_rows_cols_shape_arr_0_arr_1_shape_0_rows_cols_shape_arr_0_arr_1_shape_1_rows_cols_shape_arr_0_arr_1_shape_1_rows_cols_shape_arr_0_arr_1_shape_2_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features_result_length_n_features
f_beta_dot_x_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f_len_results_f
p_beta_dot_x_one_minus_p_beta_dot_x_dlog_p_beta_dot_x_w_i_arr
result += x.T.dot(w_i)
return result
@njit(cache=True)
def _information(x_data,
y_data,
beta):n_samples,n_features= x.shape
result=np.zeros((n_features,n_features))
f=x.dot(beta)
p=_logistic(f)
one_minus_p=1-p
dlog_p=_dlogistic(f)
w_i=p*dlog_p/y-data+(one_minus_p)*dlog_p/(one_minus_p)-data
tmp=x.multiply(np.tile(np.array([w_i]).T,(n_features)).T)#this might be wrong
tmp=tmp.T.dot(x)#this might be wrong
result+=tmp#this might be wrong
return result
n_samples,n_features= x.shape
f=x.dot(beta) #n_sample*n_feature
p=_logistic(f) #n_sample*feature
one_minus_p=1-p
dlog_p=_dlogistic(f) #n_sample*feature
w_i=p*dlog_p/y-data+(one_minus_p)*dlog_p/(one_minus_p)-data #n_sample*feature
tmp=x.multiply(np.tile(np.array([w_i]).T,(n_feature)).T)#this might be wrong #n_sample*n_feature
tmp=tmp.T.dot(x)#this might be wrong #feature*n_feature
result+=tmp#this might be wrong #feature*n_feature
return result
@njit(cache=True)
def _gradient(x,y,beta):n_samples,n_featues= x.shape
grad=np.zeros(n_featues)#shape=(features,)
f=x.dot(beta)#shape=(samples,)
p=_logistic(f)#shape=(samples,)
grad-=np.sum((y-p)*x,axis=0)/len(y)#shape=(features,)
return grad
@njit(cache=True)
def _second_derivative(x,y,beta):n_samples,n_featues= x.shape
sec_der=np.zeros((n_featues,n_featues))#shape=(features,fetures,) aka matrix shape=(features**features,)
f=x.dot(beta)#shape=(samples,)
p=_logistic(f)#shape=(samples,)
dpdf=_dlogisitic(f)#shape=(samples,)
ddpdf=_ddloigstic(f)#shape=(samples,)
term=y*p*(dpdf**(-1))-((y-pp)*(dpdf)**(-1))+((y*(pp-(pp**(-))))*(((ddpdf)/dpdf)-(dpdf**(-))))
sec_der=-np.matmul(np.transpose(x),x*(term[:,None]*dpdf[:,None]))/len(y)#shape=(features,fetures,) aka matrix shape=(features**features,)
return sec_der
# -*- coding: utf-8 -*-
import numpy as np
from numba import njit@njit()
def neg_log_likelihood(theta,x,y):
return -(np.log(_sigmoid(theta,x)).T*y).sum()@njit()
def gradient(theta,x,y):
return ((x.T)(_sigmoid(theta,x)-y)).reshape((-1,))@njit()
def hessian(theta,x,y):
return ((x.T*_sigmoid(theta,x)*(np.ones_like(_sigmoid(theta,x))-
_sigmoid(theta,x))).T*x).reshape((-1,-1))@njit()
def gradient_descent(initial_theta,alpha,num_iters,X,y):
theta=initial_theta.copy()
for i_iterate_range_num_iters:
grad=gradient(theta,X,y)
hess=hessian(theta,X,y)
hess_inv=np.linalg.inv(hess+alpha*np.eye(len(initial_theta)))
theta-=np.matmul(hess_inv,np.transpose(grad))
return theta@njit()
def newton_method(initial_theta,num_iters,X,y):
theta=initial_theta.copy()
for i_iterate_range_num_iters:
grad=gradient(theta,X,y)
hess=hessian(theta,X,y)
hess_inv=np.linalg.inv(hess+alpha*np.eye(len(initial_theta)))
step=np.matmul(hess_inv,np.transpose(grad))
while neg_log_likelihood(
theta-step,X,y)>neg_log_likelihood(
theta,X,y)-step_size*(grad.T*step)[0]:
step_size/=10
step*=step_size
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
continue
if step_size<tolerance:
break
else:
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
pass
return theta-step@njit()
def sigmoid_function(a,b,c,x):
return c/(a+b*np.exp(-x))@njit()
def sigmoid_gradient(a,b,c,x):
return c*b*np.exp(-x)/(a+b*np.exp(-x))**2@njit()
def sigmoid_hessian(a,b,c,x):
first_term=-c*b*np.exp(-x)/(a+b*np.exp(-x))**3
squared_first_term=(-c*b*np.exp(-x)/(a+b*np.exp(-x))**3)**(first_term)+c*b**(
squared_first_term)*np.exp(-x)/(a+b*np.exp(-x))**4
return squared_first_termz=sigmoid_function(a,b,c,xdata[j])
z_gradient=sigmoid_gradient(a,b,c,xdata[j])
z_hessian=sigmoid_hessian(a,b,c,xdata[j])
loglik+=np.log(z)-zdata[j]
loglik_grad-=z_gradient-zdata[j]*z_hessian*z_gradient/z**(z-
z_gradient+z_hessian*z**(z+
z_gradient-z_hessian*z))loglik_hess-=z_hessian-zdata[j]*(z**(z+
z_gradient-z_hessian*z))*(
z_gradient-z_hessian*z)**(z_gradient-z_hessian*z)+
zdata[j]*(
z**(z+
z_gradient-z_hessian*z))*((
z_gradient-z_hessian*z)**(z_gradient-z_
hessian*z))*((
z+hessian_z)**(z+hessian_z-
hessian_z*hessen_z))j+=j
raise Exception('Fitting failed')
raise Exception('Fitting failed')
raise Exception('Fitting failed')
pass
pass
