FC Cosmos Koblenz: A Comprehensive Analysis for Sports Betting

Overview of FC Cosmos Koblenz

FC Cosmos Koblenz is a prominent football team based in the city of Koblenz, Germany. Competing in the regional league, the team has established itself as a formidable opponent with a unique playing style. Founded in 2008, the club is currently managed by Coach Markus Müller. The team’s formation typically involves a 4-3-3 setup, balancing defense and attack effectively.

Team History and Achievements

Since its inception, FC Cosmos Koblenz has had a remarkable journey. Notable achievements include winning the Regional League Championship twice and consistently finishing in the top half of the league standings. The 2016 season was particularly memorable when they secured second place, narrowly missing out on promotion to a higher division.

Current Squad and Key Players

The current squad boasts several standout players. Top performers include striker Lukas Schmidt, known for his goal-scoring prowess, and midfielder Thomas Bauer, who excels in playmaking. Defender Erik Müller is crucial for maintaining a solid defensive line.

Team Playing Style and Tactics

FC Cosmos Koblenz employs an aggressive 4-3-3 formation, focusing on quick transitions from defense to attack. Their strengths lie in their fast-paced offensive strategies and disciplined defense. However, they occasionally struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “The Cosmic Warriors,” reflecting their dynamic playing style. They have a passionate fanbase known as “The Celestial Supporters.” A fierce rivalry exists with FC Rheinland Mainz, adding excitement to their matches.

Lists & Rankings of Players and Performance Metrics

• Lukas Schmidt: Leading goalscorer 🎰
• Thomas Bauer: Top assist provider 💡
• Erik Müller: Best defender ✅

Comparisons with Other Teams in the League

In comparison to other teams in their division, FC Cosmos Koblenz stands out for their attacking flair but sometimes lacks consistency in defense compared to more defensively oriented teams like SV Trier.

Case Studies or Notable Matches

A notable match was against FC Rheinland Mainz in 2020, where they secured a thrilling 3-2 victory that showcased their resilience and tactical acumen.

Tables Summarizing Team Stats and Recent Form

Statistic	Last Season	This Season (so far)
Total Goals Scored	45	20
Total Goals Conceded	30	15
Last Five Matches Form (W-D-L)	N/A	3-1-1
Odds for Next Match Win/Loss/Draw (Home/Away)	N/A/N/A/N/A/N/A/N/A/N/A/N/A/N/A/1.5/3.0/4.0/1.7/3.5/4.5/1.6/3.3/4.8/N/A/N/A/N/A/N/A/N/A/N/A/N/A/N/A/N/A/N/A(N)/N>A/W/D/L)	N/D/L)	N/D/L)	N/D/L)	N/D/L)	N/D/L)	N/D/L)

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

1. Analyze recent form trends to gauge momentum.
2. Pay attention to key player performances; Lukas Schmidt’s form can significantly impact match outcomes.
3. Bet on games against weaker opponents when key players are fit.
4. Maintain awareness of upcoming fixtures against rivals like FC Rheinland Mainz due to heightened competitive intensity.

[0]: # Copyright (c) Facebook, Inc. and its affiliates.
[1]: #
[2]: # This source code is licensed under the MIT license found in the
[3]: # LICENSE file in the root directory of this source tree.

[4]: import torch
[5]: from fairseq import metrics

[6]: from . import FairseqCriterion

[7]: def _compute_loss(logits: torch.Tensor,
[8]: targets: torch.Tensor,
[9]: ignore_index: int,
[10]: weight: torch.Tensor = None,
[11]: reduce=True):
[12]: “””Compute loss given prediction scores / logits and targets.

[13]: Args:
[14]: logits: prediction scores / logits
[15]: targets: target labels
[16]: ignore_index: index that is ignored while computing loss
[17]: weight: loss weight for each token
[18]: reduce (bool): if set to False return loss per batch element instead of
[19]: averaging

[20]: Returns:
[21]: torch.Tensor: loss.
[22]: “””
[23]: losses = -torch.gather(logits.transpose(1, 2), dim=2,
[24]: index=targets.unsqueeze(1)).squeeze(1)

if not reduce:

loss_per_batch_element = losses.clone()
losses = None
return loss_per_batch_element.contiguous()

if weight is not None:

losses = losses * weight.type_as(losses).unsqueeze(-1)

if ignore_index >= 0:

pad_mask = targets != ignore_index

losses = losses.masked_fill(~pad_mask,
0.)
n_tokens = pad_mask.sum().type_as(losses)

if n_tokens == 0 or not reduce:
return (
losses.sum() if reduce else losses)

else:

if n_tokens > 0:

stefanocua/fairseq<|file_sep satisfied requirement")
raise RuntimeError(msg)

def check_argument_types():

def warn_missing_dependency(module_name):

class FairseqTask(FairseqTaskMixin):

class FairseqTaskRegistry(object):

class CheckpointManager(object):

class DistributedCheckpointHook(CheckpointHook):

class CheckpointHook(object):

def register_checkpoint_hooks(*checkpoint_hooks):

def get_default_architecture(args):

def build_model(args, task=None):

def load_model_ensemble(model_paths,
arg_overrides=None,
task=None,
ensemble_transforms=None,
remove_pretraining_modules=False):

def load_model_ensemble_from_hf_hub(
repo_id_or_path,
use_auth_token=None,
revision='main',
cache_dir=None,
load_checkpoint_heads=True,
args_overrides=None):

def save_model(model_or_ensemble_of_models,
save_dir_path_or_file_obj_or_bufferlike_obj_or_stringpathlikeobj_or_filelikeobj,
filename_prefix='',
file_friendly_logging=False):

def load_alignments(filename):

def save_alignments(filename,

alignments,

order=None):

def print_aligned_sentences(sentences,

alignments,

output_stream=sys.stdout,

max_len_a=80,

max_len_b=100,

):

def print_sents(sents,

output_stream=sys.stdout,

):

def parse_metrics(logs_str):

def aggregate_logs(logs_list):

def aggregate_log_json(logs_json_list):

class Metric(object):

class AverageMetric(Metric):

class CorrelationMetric(Metric):

class FMeasureMetric(Metric):

class BLEU(Metric):

class GLEU(Metric):

class ROUGEL(Metric):

class ROUGEALL(Metric):

class SacreBLEU(Metric):

class TERMetric(Metric):

@dataclasses.dataclass(frozen=True)
class AggregatorConfig:

@dataclasses.dataclass(frozen=True)
@register_serializable(_SERIALIZABLE_REGISTRY)
@developer_checkpoints.register("default")
@metrics.register("default")
@metrics.Aggregator.register("default")
@checkpoints.register("default")

stefanocua/fairseq None:
# super().__init__(*args,**kwargs)

# def __getattr__(self,name)->pd.DataFrame:
# try :
# return self[name]
# except KeyError :
# raise AttributeError(name)

# def dataframes_from_dict(dicts :list) -> list :
# dfs : list = []
# keys : set = set()
# for d in dicts :
# keys.update(set(d.keys()))

# keys.sort()

# for d in dicts :
# dfs.append(DataframeFromDict({k:d[k] if k in d else np.nan for k in keys}))

# return dfs

### Return : [n_sample,n_metric] matrix with sample mean , std , df , t-statistics , p-values , effect sizes

### Each column correspond to one metric

### Each row correspond to one sample

#### If only one sample : only mean,std are returned

#### If two samples : mean,std,t-statistics,p-values,effect_sizes are returned

#### If more than two samples : mean,std,t-statistics,p-values,effect_sizes are returned

#### But t-statistics,p-values,effect_sizes columns corresponds respectively to :

##### First vs Second Sample

##### First vs Third Sample

##### …

##### Last but One vs Last Sample

### Columns names :

#### Mean columns names :

##### ‘Mean_Sample_X_Metric_Y’ where X goes from first sample index until last sample index , Y goes from first metric name until last metric name

#### Std columns names :

##### ‘Std_Sample_X_Metric_Y’ where X goes from first sample index until last sample index , Y goes from first metric name until last metric name

#### T-statistics columns names :

##### ‘Tstat_Sample_X_vs_Sample_Y_Metric_Z’ where X goes from first sample index until second last sample index , Y goes from second sample index until last sample index , Z goes from first metric name until last metric name

#### P-value columns names :

##### ‘Pvalue_Sample_X_vs_Sample_Y_Metric_Z’ where X goes from first sample index until second last sample index , Y goes from second sample index until last sample index , Z goes from first metric name until last metric name

#### Effect sizes columns names :

##### ‘EffectSize_Sample_X_vs_Sample_Y_Metric_Z’ where X goes from first sample index until second last sample index , Y goes from second sample index until last sample index , Z goes from first metric name until last metric name

### Indexes are just numbers starting at zero corresponding respectively to each samples indexes

### Rows are just numbers starting at zero corresponding respectively to each metrics indexes

def stats(df_dict:list)->np.ndarray:

### Get number of samples

N_samples:int = len(df_dict)

### Get all metrics

metrics:list = df_dict[N_samples – 1].columns.tolist()

### Create empty dataframe which will contain all statistics results

result_df=pd.DataFrame(index=np.arange(len(metrics)),dtype=np.float64)

### Fill empty dataframe with means

means:list=[df.mean(axis=0).tolist()for df in df_dict]

### Fill empty dataframe with stds

stds:list=[df.std(axis=0).tolist()for df in df_dict]

result_df[[f’Standart Deviation_{metric}’for metric in metrics]]=np.array(stds).transpose()

### If only one samples : Return means + stds

if N_samples==1:

return result_df.values.transpose()

else:

### Else fill empty dataframe with t-statsitics,p-values,effect sizes between each pair of samples

for i,sample_i_idx_i,sample_i_idx_j,matrix_i,matrix_j,matrix_k,result_row_i,result_row_j,result_row_k,matrix_i_mean,matrix_j_mean,matrix_i_std,matrix_j_std,N_sample_i,N_sample_j,i<j<N_samples,j<N_samples,k<N_samples,i<j<k:N_samples,i<j<N_samples,j<k:N_samples,k<N_samples,i<k<N_samples,j<k:N_samples:

mean_matrix_i=np.mean(matrix_i,axis=0)

mean_matrix_j=np.mean(matrix_j,axis=0)

std_matrix_i=np.std(matrix_i,axis=0)

std_matrix_j=np.std(matrix_j,axis=0)

N_sample_i=len(matrix_i)

N_sample_j=len(matrix_j)

t_statistic,p_value=ttest_ind_from_stats(mean_matrix_i,std_matrix_i,N_sample_i,
mean_matrix_j,std_matrix_j,N_sample_j)

effect_size=(mean_matrix_i-mean_matrix_j)/np.sqrt((std_matrix_i**2+std_matrix_j**2)/
(N_sample_i+N_sample_j-
2))

result_row_k['Tstatistic']=t_statistic.tolist()

result_row_k['Pvalue']=p_value.tolist()

result_row_k['EffectSize']=effect_size.tolist()

result_df[[f'Tstatistic_{sample_idx}_{metric}'for
metric

]]=np.array(result_row_k['Tstatistic']).reshape(-1,len(metrics))

result_df[[f'Pvalue_{sample_idx}_{metric}'for
metric

]]=np.array(result_row_k['Pvalue']).reshape(-1,len(metrics))

result_df[[f'EffectSize_{sample_idx}_{metric}'for
metric

]]=np.array(result_row_k['EffectSize']).reshape(-1,len(metrics))

return result_df.values.transpose()

stefanocua/fairseq<|file_sep[
{
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"name": "transformer_wmt_en_de_big",
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"test": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/wmt14.en-de.joined-dict.de-en"
},
"model_file": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/de-en/checkpoints/checkpoint_best.pt",
"config_file": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/de-en/config.json"
},
{
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"architectures": [
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],
"dataset": {
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"valid": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/wmt14.en-de.joined-dict.de-en-nobpe",
"test": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/wmt14.en-de.joined-dict.de-en-nobpe"
},
"model_file": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/de-en-nobpe/checkpoints/checkpoint_best.pt",
"config_file": "/mnt/nfs/work/skial/huggingface_datasets/wmt14/de-en-nobpe/config.json"
},
{
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]
},
{
"langpair":"en-fr",
"models":[
{
"name":"transformer_wmt_en_fr_big","architectures":["transformer"],"dataset":{"train":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.fren/en-fr/train","valid":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.fren/en-fr/dev","test":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.fren/en-fr/test"},"model_file":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.fren/en-fr/model/checkpoint_best.pt","config_file":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.fren/en-fr/config.json"}
]
},
{
"langpair":"en-es",
"models":[
{
"name":"transformer_wmt_en_es_big","architectures":["transformer"],"dataset":{"train":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.spanish/es-en/train","valid":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.spanish/es-en/dev","test":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.spanish/es-en/test"},"model_file":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.spanish/es-en/model/checkpoint_best.pt","config_file":"/home/dluser/workspace/data/fairseq_data/coco-wiki-detok.spanish/es-en/config.json"}
]
},
{
"langpair":"en-arabic",
"models":[
{
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"architectures":["wmts_arabic_parallel_corpus_arabic_translation_to_english_transformers_12layers_transformers-transformers-model"],
"dataset":{
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]
},
{
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}
]stefanocua/fairseq# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math

import numpy as np

from fairseq import utils

EPSILON = float(np.finfo(float).eps)

@register_task(‘mask_prediction’)
class MaskPredictionTask(FairseqTask):

def __init__(self,args,criterion_class=CategoricalCrossEntropyCriterion):

self.criterion_class=criterion_class

self.mask_ratio=args.mask_ratio

self.random_ratio=args.random_ratio

self.leave_unmasked=args.leave_unmasked

self.noise_ratio=args.noise_ratio

def setup_task(self,args,data_cfg,path_cfg,model_cfg,cfg_cache=None)->None:

if self.mask_ratio=float(‘inf’):

raise ValueError(f’mask ratio {self.mask_ratio} must be within (epsilon,infinity)’)

elif self.random_ratio=float(‘inf’):

raise ValueError(f’random ratio {self.random_ratio} must be within (epsilon,infinity)’)

elif self.noise_ratio=float(‘inf’):

raise ValueError(f’noise ratio {self.noise_ratio} must be within (epsilon,infinity)’)

elif not isinstance(self.criterion_class,CategoricalCrossEntropyCriterion):

raise TypeError(f’criterion class {self.criterion_class} must be an instance of CategoricalCrossEntropyCriterion’)

if self.mask_ratio+self.random_ratio+self.leave_unmasked+self.noise_ratio!=float(1.):

raise ValueError(f’mask ratio {self.mask_ratio}+random ratio {self.random_ratio}+leave unmasked ratio {self.leave_unmasked}+noise ratio{self.noise_ration} must sum up exactly to one’)

if not hasattr(self,’task’):

self.task=FairSeqTaskRegistry.get_task(‘language_modeling’,args,data_cfg,path_cfg,model_cfg,cfg_cache=self.cfg_cache)

def build_criterion(self,criterion_cfg)->CategoricalCrossEntropyCriterion:

return criterion_class.build_criterion(criterion_cfg,self.task.target_dictionary)

def build_generator(self,model_cfg)->MaskPredictionGenerator:

return MaskPredictionGenerator(self.task.target_dictionary,self.mask_probability,self.random_probability,self.leave_unmasked_probability,self.noise_probability)

def max_positions(self)->int:

return min([self.task.max_positions(),int(math.floor(1024/self.model_cfg.decoder.embed_dim))])

@register_criterions(‘mask_prediction’)
@register_criterion(‘mask_prediction’)
@metrics.aggregate(‘accuracy’,’loss’)
@metrics.aggregate(‘perplexity’,’loss’)
@metrics.aggregate(‘token_accuracy’,’accuracy’)
@metrics.aggregate(‘token_error’,’accuracy’)
@metrics.aggregate(‘sequence_accuracy’,’accuracy’)
@metrics.aggregate(‘sequence_error’,’accuracy’)
@utils.register_full_closure_args([‘generator’])
@utils.register_full_closure_args([‘task’])
class CategoricalCrossEntropyCriterion(FairSeqCriterion):

def __init__(self,criterion_cfg,tgt_dict,generator=None,**kwargs)->None:

def forward(self,model,input,target,**unused)->Tuple[Tensor,Tensor]:

log_probs=model.get_normalized_probs(input,target_lengths=input.shape[-1],log_probs=True)

target=target.reshape(-1)

nll=-log_probs.reshape(-1,target.shape[-1])[range(target.shape[-1]),target]

nll=nll.reshape(input.shape[:-1])

lprobs=torch.exp(log_probs)

target=lprobs.reshape(-1,target.shape[-1])[range(target.shape[-0]),target].reshape(input.shape[:-1])

if model.training:

mask=torch.ones_like(nll).bool()

mask=~target.bool()

mask[mask]=~generator.draw(mask[mask].shape)

mask[mask]=(~generator.draw(mask[mask].shape))&target[mask]

mask[mask]=generator.draw(mask[mask].shape)&(~target[mask])

replacement_dist=gensim.utils.softmax(np.full(generator.tgt_dict.sizes(),fill_value=model.generator.shared.weight.detach().cpu().numpy().max()),axis=-0)

replacement_dist/=replacement_dist.sum(axis=-1)[…,None]

replacement_dist=torch.from_numpy(replacement_dist.astype(np.float32)).to(nll.device)

nll[mask]=-(replacement_dist.log()[…,generator.draw(replacement_dist.size()[0]).long()][range(replacement_dist.size()[0]),target[mask]])

return nll,targtet,lprobs

def aggregate_logging_outputs(cls=logging_outputs,norm_by_src_len=False,norm_by_tgt_len=False)->dict:

if norm_by_src_len or norm_by_tgt_len:

length=getattr(logging_outputs,’tgt’,None)

if length is None:

length=getattr(logging_outputs,’ntokens’,None)

if length is None:

raise Exception(f’unable to normalize because neither src nor tgt lengths were found’)

else:

length=float(length)

log_denom=length*getattr(logging_outputs,’nsentences’,None)

for log_output_key,value in logging_outputs.items():

logging_outputs=log_output_key*(value/log_denom)

continue

break

else:

length=float(length)

log_denom=length*getattr(logging_outputs,’nsentences’,None)

for log_output_key,value,_length_value,_num_sentences_value,_denominator_value,_denominator_key_value,_numerator_key_value,_numerator_value_value,_numerator_denominator_value,_denominator_num_sentences_value,_numerator_num_sentences_value,_denominator_denominator_num_sentences_value,_numerator_numerator_num_sentences_value,_denominator_denominator_denominator_num_sentences_value,_numerator_numerator_numerator_num_sentences_values=_logging_output:

logging_outputs=log_output_key*(value/log_denom)

continue

break

else:

length=float(getattr(logging_output,’tgt_len’,None))

log_denom=length*getattr(logging_output,’sent_num’,None)

for log_output_key,value,length,num_sent,dem,dem_key,num_dem,num_val,dem_val,num_dem_sent,dem_dem_sent,num_dem_dem_sent,dem_dem_dem_sent,num_dem_dem_num_sent=dem_dem_dem_sent:

logging_outputs=log_output_key*(value/log_denom)

continue