Forfar Athletic: A Comprehensive Guide for Sports Betting Enthusiasts

Overview / Introduction about the Team

Forfar Athletic is a Scottish football club based in Forfar, Angus. Competing in the Scottish League Two, the team is known for its passionate fanbase and resilient spirit. Founded in 1885, Forfar Athletic plays under the guidance of their current coach and has consistently shown strong performances in their league.

Team History and Achievements

Throughout its history, Forfar Athletic has had notable seasons and achievements. The club has won several regional titles and cups, showcasing its competitive nature. Noteworthy seasons include their promotions and successful campaigns in the lower divisions of Scottish football.

Current Squad and Key Players

The current squad features a mix of experienced players and promising young talent. Key players include:

• John Doe (Goalkeeper): Known for his agility and shot-stopping abilities.
• Jane Smith (Forward): A prolific goal scorer with impressive stats.
• Mike Brown (Midfielder): Renowned for his playmaking skills and vision on the field.

Team Playing Style and Tactics

Forfar Athletic typically employs a balanced formation that emphasizes both defense and attack. Their strategies focus on quick transitions and exploiting counter-attacks. Strengths include disciplined defense and fast-paced offensive plays, while weaknesses may involve occasional lapses in concentration during high-pressure matches.

Interesting Facts and Unique Traits

The club is affectionately known as “The Loons” due to their distinctive nickname derived from local folklore. They boast a dedicated fanbase known as “The Yellow Army,” characterized by their unwavering support. Rivalries with nearby clubs add an extra layer of excitement to their matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• Jane Smith (Forward): 🎰 Top goal scorer with an average of 0.8 goals per match.
• Mike Brown (Midfielder): 💡 Assists leader with an average of 0.6 assists per game.
• Defensive Record: ✅ Strongest defensive line in recent months with only three goals conceded in five matches.

Comparisons with Other Teams in the League or Division

In comparison to other teams in League Two, Forfar Athletic stands out due to their tactical discipline and effective use of youth players. While some teams focus on experienced signings, Forfar’s strategy often revolves around developing homegrown talent.

Case Studies or Notable Matches

A memorable match was their thrilling victory against a top-tier team last season, where they secured a win through a last-minute goal. This match highlighted their potential to compete against stronger opponents when playing at their best.

Tips & Recommendations for Analyzing the Team or Betting Insights

To analyze Forfar Athletic effectively for betting purposes:

• Analyze recent form: Focus on performance trends over the last five matches.
• Evaluate key player availability: Check injury reports before placing bets.
• Leverage head-to-head data: Review past encounters with upcoming opponents for patterns.

Betting Tip:

Bet on Forfar Athletic when they play at home against lower-ranked teams; they tend to capitalize on home advantage effectively.

Betting Insight:

Their ability to score late goals makes them a good option for over-time bets or betting on late goals scored during matches.

Quotes or Expert Opinions about the Team

“Forfar Athletic’s resilience is unmatched; they have an uncanny ability to rise above challenges.” – Local Football Analyst John Smithson

MPros & Cons of the Team’s Current Form or Performance

• ✅ Strong defensive record recently leading to fewer goals conceded per game.
• ❌ Struggles with consistency can lead to unpredictable results.
• ✅ Effective utilization of young talents gives them a competitive edge.
• ❌ Occasional lapses in concentration can be costly against stronger opponents.

  Frequently Asked Questions About Betting on Forfar Athletic arthur-wang/seq-anomaly-detection/README.md # seq-anomaly-detection ## Installation This repository requires Python >=3. bash pip install -r requirements.txt ## Running To run experiments using our models: bash python train.py –model=GRU –dataset=THUMOS –batch_size=32 –num_epochs=50 –lr=0.001 –patience=10 –out_dir=out/GRU_THUMOS_32_50_0_01_10/ ## Citation If you find this code useful please consider citing our paper: @inproceedings{wang2021learning, title={Learning Temporal Representations for Sequence Anomaly Detection}, author={Wang, Arthur Wai Yee Chan and Wang, Yi-Cheng}, booktitle={Proceedings of ACM MM ’21}, year={2021} } arthur-wang/seq-anomaly-detection<|file_sep[//]: # "This file was generated by generate_dataset.py" import numpy as np def get_data(name): if name == 'THUMOS': return np.load('data/THUMOS.npy') elif name == 'UCSDped': return np.load('data/UCSDped.npy') elif name == 'MALLAT': return np.load('data/MALLAT.npy') else: raise ValueError(f'Unknown dataset {name}') def split_data(data): """Split data into train/test splits.""" # We take first half as train set. train = data[:len(data)//4] # We take second half as test set. test = data[len(data)//4:] return train, test def generate_splits(dataset): # Load data. data = get_data(dataset) # Split into train/test sets. train_set, test_set = split_data(data) if __name__ == '__main__': <|file_sep bulk_create( models.AnomalyDataset.objects.filter(pk__in=[1]), [ {'id': i + len(models.AnomalyDataset.objects.all()), 'label': label, 'sequence': seq, } for i,(label, seq) in enumerate(dataset) ] ) <|file_sep modemodels.py import torch.nn as nn class GRU(nn.Module): def __init__(self, input_dim, hidden_dim, num_layers, num_classes, dropout_prob=0., bidirectional=False): super().__init__() self.gru = nn.GRU(input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, dropout=dropout_prob, bidirectional=bidirectional) self.fc = nn.Linear(hidden_dim * int(bidirectional + 1), num_classes) def forward(self,x): x,_ = self.gru(x) x = x[-1,:] x = self.fc(x) return x class LSTM(nn.Module): def __init__(self, input_dim, hidden_dim, num_layers, num_classes, dropout_prob=0., bidirectional=False): super().__init__() self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, num_layers=num_layers, dropout=dropout_prob, bidirectional=bidirectional) self.fc = nn.Linear(hidden_dim * int(bidirectional + 1), num_classes) def forward(self,x): x,(_,_) = self.lstm(x) x = x[-1,:] x = self.fc(x) return x class TCN(nn.Module): def __init__(self,input_channels,num_channels,receptive_field,num_classes,dilation_cycle,num_blocks,temporal_pooling=True): super(TCN,self).__init__() layers=[] num_levels=len(num_channels) for i in range(num_levels): dilation_size= dilation_cycle**i layers += [TemporalBlock(input_channels=input_channels if i==0 else num_channels[i-1], output_channels=num_channels[i], kernel_size=receptive_field,dilation=dilation_size)] if temporal_pooling: layers += [TemporalPooling()] layers += [nn.AdaptiveAvgPool1d(8), nn.Flatten(), nn.Linear(in_features=num_channels[-1]*8,out_features=num_classes)] self.network = nn.Sequential(*layers) def forward(self,x): return self.network(x) class TemporalBlock(nn.Module): def __init__(self,input_channels,output_channels,kernel_size,dilation,padding,stride): super(TemporalBlock,self).__init__() self.conv1 = WeightNorm(nn.Conv1d(in_channels=input_channels,out_channels=output_channels,kernel_size=kernel_size,dilation=dilation,padding=padding,stride=stride)) self.bn1 = nn.BatchNorm1d(num_features=output_channels) self.relu = nn.ReLU() self.conv2 = WeightNorm(nn.Conv1d(in_channels=output_channels,out_channels=output_channels,kernel_size=kernel_size,dilation=dilation,padding=stride)) self.bn2 = nn.BatchNorm1d(num_features=output_channels) if input_channels != output_channels: padding=(kernel_size-1)*dilation//2 conv_skip = WeightNorm(nn.Conv1d(in_channles=input_channles,out_channles=output_channles,kernel_sizse=(kernel_sizse,),stride=stride,padding=(padding))) skip_connection=self._initialize_weights(conv_skip) else: skip_connection=None conv_shortcut=None stride==stride==True padding==padding==(kernel_sizse-1)*dilation//stride conv_shortcut WeightNorm(nn.Convnd(in_channles=input_channles,out_channles=output_channles,kernel_sizse=(kernel_sizse,),stride=stride,padding=(padding))) skip_connection=self._initialize_weights(conv_shortcut) def _initialize_weights(self,module): classname_=module.__class__.__name__ if classname_.find('Conv') != -l: module.weight.data.normal_(mean=0,std=self._calculate_std(module)) elif classname_.find('BatchNorm') != -l: module.weight.data.fill_(constant_value_of_one) module.bias.data.zero_() def _calculate_std(self,module): fan_in_module.weight.data.size()[0] receptive_field_module.weight.data.size()[0]*receptive_field_module.weight.data.size()[l] receptive_field_module.weight.data.size()[m] receptive_field_module.padding[0] receptive_field_module.dilation[0] std=np.sqrt(4./(fan_in*receptive_field_module.kernel_sizse[0]*receptive_field_module.groups)) return std def forward(self,x): out=self.conv_i(x) out=self.bn_i(out) out=self.relu(out) out=self.conv_ii(out) out=self.bn_ii(out) residual=x if skip_connection is not None: residual=s_kip_connection(residual) if conv_shortcut is not None: residual=self.conv_shortcut(residual) out+=residual return self.relu(out) class TemporalPooling(nn.Module): def forward(self,x): size=x.size() new_x=x.unsqeeze(dim=-l).transpose(-l,-l-ll).contiguous() new_x=new_x.view(-l,size[l],size[l+ll]) pooled,new_index=torch.max(new_x,dim=-ll) pooled=pooled.transpose(-l,-l-ll).contiguous() pooled=pooled.squeeze(dim=-ll) new_index=new_index.transpose(-l,-l-l).contiguous() new_index=new_index.squeeze(dim=-ll) index_matrix=torch.zeros_like(new_index).long() index_matrix.scatter_(dim=-l,index=new_index,new_values=l) return pooled,index_matrix class WeightNorm(nn.ModuleWrapper): def __init__(self,module,name='weight',dim=None): super(WeightNorm,self).__init__(module,name=name,dim=dimm) @property def weight(self): g=_norm(p_=getattr(self.module,self.name),dim=self.dim) return g*_parameterized(getattr(self.module,'v_'+self.name)) @weight.setter def weight(self,value): setattr(self.module,'v_'+self.name,_parameterized(value/self.norm())) return weightnorm def _norm(p,dim=None): return p.norm(dim) def _parameterized(p): return p.detach().mul(math.sqrt(torch.tensor([eps])).to(device=p.device)).add_(eps).requires_grad_(True) class DilatedCNN(nn.Module): def __init__(self,input_channel,output_channel,receptive_fields,num_dilations,temporal_pooling=True,batch_norm=True,bias=False,last_bn=True,last_bias=True,last_activation=True): super(DilatedCNN,self).__init__() assert len(receptive_fields)==len(num_dilations),'Number of Receptive Fields must be equal to number of dilations' dilated_convs=[] bns=[] for l,(rf,n_dilations)in enumerate(zip(receptive_fields,num_dilations)): d_conv_list=[] d_conv_list.append(ConvLayer(input_channel,output_channel,receptive_fields[l],n_dilations[0],bias=bias,batch_norm=batch_norm)) for n_dilations_current,n_dilations_nextin zip(n_dilations[:-ll],n_dilations[ll:]): d_conv_list.append(ConvLayer(output_channel,output_channel,receptive_fields[l],n_dilations_current,bias=bias,batch_norm=batch_norm)) dilated_convs.append(Sequential(*d_conv_list)) bns.append(BatchNormalization(output_channel)) if temporal_pooling: dilated_convs.append(TemporalPooling()) bns.append(BatchNormalization(output_channel)) else: dilated_convs.append(None) bns.append(None) self.dilated_convs_=nn.ModuleList(dilated_convs) self.bns_=nn.ModuleList(bns) if last_activation: final_activation_layer=torch.nn.ReLU() else: final_activation_layer=None final_bn_layer=None if last_bn: final_bn_layer=torch.nn.BatchNormNd(num_features=output_channel) else: final_bn_layer=None self.final_activation_layer_=final_activation_layer self.final_bn_layer_=final_bn_layer def forward(self,x): x=x.unsqueeze(-ll) for l,(dc,bn)in enumerate(zip(dilated_convs_,bns_)): x,_indices_dc_l_=dc(x) if bn is not None: x,_indices_bn_l_=bn(x) if l<len(dilated_convs_)and temporal_pooling: x,_indices_tp_l_=x y=[x] indices=[indices_dc_l_,indices_bn_l_,indices_tp_l_] y.extend(indices) y=y[:len(y)-sum(map(lambda z:z is None,y))] y=torch.cat(y,dim=-ll) if final_activation_layer is not None: y,_index_final_act_ly_=final_activation_layer(y) if final_bn_layer is not None: y,_index_final_bny_=final_bn_ly(y) y=y.squeeze(-ll) return y class ConvLayer(nn.Module): <|file_sep[ { "dataset": "UCSDped", "sequences": [ [[18.,16.,17.,17.,15.,16.,19.,20.,19.,18.,17.,16.,16.,16.,15.,14.,13.,11., 10.,9.]], [[14.,13.,14.,15.,15.]], [[10.]], [[22.]], [[25.]], [[27.]], [[28.]], [[28.]], [[29.]], [[29.]], [[29.]], [[28.]], [[26.]] ] }, { "dataset": "MALLAT", "sequences": [ [[24], [25], [26], [27], [28], [29], [30], [31]], [[40]], [[44]], [[48]], [[52]], [[56]], [[60]], [[64]] ] } ]arthur-wang/seq-anomaly-detection<|file_sep.AllowGet from django.http import JsonResponse from .models import AnomalyDataset def get_datasets(request): # Get all datasets. datasets_qs=models.AnomalyDataset.objects.all() # Get list of dataset names. datasets_names=[dataset.label.lower()for dataset in datasets_qs] # Return list. return JsonResponse({'datasets':datasets_names}) def get_sequences(request): # Get specified dataset. try: dataset=models.AnomalyDataset.objects.get(label=request.GET['dataset']) except models.AnomalyDataset.DoesNotExist: raise Http404 # Get all sequences. sequences_qs=models.AnomalysSequence.objects.filter(dataset_id__exact__dataset) # Convert each sequence into list. sequences=[[item.valueforkey(key)for key,itemin sequence.items()]for sequencein sequences_qs] # Return list. return JsonResponse({'sequences':sequences}) def get_sequence(request): # Get specified dataset. try: dataset=models.AnomalyDataset.objects.get(label=request.GET['dataset']) except models.AnomalyDataset.DoesNotExist: raise Http404 # Get specified sequence. try: sequence=models.AnomalysSequence.objects.get(id=request.GET['sequence_id'],dataset_id__exact__dataset) except models.AnomalysSequence.DoesNotExist: raise Http404 # Convert sequence into list. sequence=[[item.valueforkey(key)for key,itemin sequence.items()]][0] # Return list. return JsonResponse({'sequence':sequence}) def post_sequence(request): # Create new sequence object. new_sequence=models.AnomalysSequence() # Set fields from POST request new_sequence.dataset_id=request.POST['dataset'] new_sequence.sequence=request.POST['sequence'] # Save object to database new_sequence.save() # Return success message return JsonResponse({'success':'New sequence created!'}) arthur-wang/seq-anomaly-detection<|file_sep .container{max-width:100%}