1. Bundesliga stats & predictions

Overview: The Berlin Recycling Volleys, known for their strong defensive play, face off against the dynamic offense of United Volleys Frankfurt. This matchup promises to be a tactical battle with both teams looking to assert dominance.

Betting Predictions:

• Winning Team: Berlin Recycling Volleys - Their home advantage and solid defense give them the edge.
• Over/Under Total Points: Over 23 - Both teams have potent offenses that can rack up points quickly.
• Top Scorer: Lukas Kampa (Berlin) - Known for his consistent scoring ability.

Tactical Insights:

Berlin's strategy will likely focus on minimizing errors and capitalizing on counterattacks. Frankfurt, on the other hand, will aim to exploit any gaps in Berlin's defense with their fast-paced plays.

Overview: VfB Friedrichshafen brings their experience and technical prowess to this game against the young and energetic Moerser SC. Expect a clash of styles as these two teams vie for victory.

Betting Predictions:

• Winning Team: VfB Friedrichshafen - Their seasoned players and strategic depth make them favorites.
• Total Sets Played: Under 5 - Both teams are expected to secure wins in straight sets.
• MVP Prediction: Sebastian Schwarz (Friedrichshafen) - His leadership and skill could be decisive.

Tactical Insights:

Friedrichshafen will likely rely on their veteran players to control the tempo, while Moerser SC will try to disrupt their rhythm with aggressive plays.

Overview: Dresdner SC's powerful front row faces Lüneburg United's versatile back row in a match that could go either way. Both teams have shown resilience throughout the season.

Betting Predictions:

• Tie/Break Winner: Dresdner SC - Their experience in close matches gives them an advantage.
• Aces Prediction: Over 10 combined aces - Both teams have strong servers capable of delivering powerful serves.
• Serving Errors: Under 20 combined - Both teams maintain composure under pressure.

Tactical Insights:

Dresdner SC will focus on maximizing their serving efficiency, while Lüneburg United will look to neutralize their opponents' key players through strategic blocking.

Lukas Kampa (Berlin Recycling Volleys)

Kampa is renowned for his consistent performance at the net, often being a crucial factor in Berlin's offensive strategies. His ability to read plays and execute precise attacks makes him a formidable opponent.

Sérgio Nogueira (VfB Friedrichshafen)

Nogueira brings international experience and a unique skill set that enhances Friedrichshafen's gameplay. His versatility allows him to adapt quickly during matches, making him a valuable asset.

Raphaela Folie (Dresdner SC)

Folie is one of the standout talents in German volleyball, known for her exceptional blocking skills and quick reflexes. Her presence in the back row often shifts the momentum in favor of Dresdner SC.

To maximize your betting potential, consider these strategies tailored for tomorrow’s games based on current form, head-to-head records, and statistical analysis.

• Analyzing Head-to-Head Records: Evaluate past encounters between teams to identify patterns or trends that could influence outcomes.
• Focusing on Player Performances: Select bets based on individual player statistics such as spikes per game or blocks made per set.
• Diversifying Bets: Distribute your wagers across different types of bets—winning team, total points scored—to spread risk while increasing potential returns.

Incorporating these strategies into your betting approach can enhance your chances of success as you enjoy tomorrow’s exciting volleyball action!

Berlin Recycling Volleys vs. United Volleys Frankfurt Statistics

• Total Points Scored Last Season:
  • Berlin: 2430 points (avg 15 per set)

• Total Points Scored Last Season:
  • Frankfurt: 2380 points (avg 14 per set)

• Aces Per Game:
  1. Berlin – Avg 8 Aces/Match
     

• Aces Per Game:
  Frankfurt – Avg 7 Aces/Match
  

• Serve Efficiency:
  Volleyball Serve Efficiency Ratio:
  Berlin – .75
  Frankfurt – .70
  

The statistics highlight both teams' strengths; however slight advantages lie with Berlin due primarily because they possess superior serve efficiency ratios compared with Frankfurt’s figures from last season’s data collection period.

This comprehensive breakdown should help inform decisions whether placing bets or simply enjoying watching these skilled athletes compete!

VfB Friedrichshafen vs. Moerser SC Statistics

• Total Blocks Last Season:
  VfB Friedrichshafen – Avg 12 Blocks/Set
  

• Total Blocks Last Season:
  Mörscher Sport Club – Avg 9 Blocks/Set
  

• Spike Accuracy:
  VfB Friedrichshafen – .62 Successful Spikes/
  Total Attempts
  
  Mörscher Sport Club – .58 Successful Spikes/
  Total Attempts
  
  

The data suggests that VfB Friedrichshafen holds an edge over Mörscher Sport Club when it comes down specifically towards defensive capabilities like blocking; however spike accuracy remains relatively close between both sides.

This detailed statistical comparison offers valuable insights into how each team might perform tomorrow night!

Dresdner SC vs Lüneburg United Statistics

• Total Assists Per Set Last Season:
  Dresden Sports Club (DSC) – Avg 18 Assists/Set
  Lüneburg Unites (LUU) – Avg16 Assists/Set
  
  This statistic highlights Dresden’s capability within playmaking roles compared against its rival counterparts.
  
  Additionally,
  jacobgraham/cellmoc<|file_sep|>/cellmoc.py # -*- coding:utf-8 -*- from __future__ import division import numpy as np import pandas as pd from collections import OrderedDict def get_allele_specific_counts_from_mpileup(filename): """ Given mpileup file from samtools mpileup function, return allele-specific counts. Args: filename(str): path+filename of mpileup file Returns: A dictionary containing allele-specific counts, where keys are genomic positions. d = { 'chr': ['chrX', 'chrX', 'chrX', ...], 'pos': [10000L, ..., ...], 'A,C': [[0L ,1L], [5L ,7L], ...] } """ with open(filename) as f: f.readline() for line in f: if line[0] == '#': continue line = line.strip().split('t') pos = int(line[1]) ref_base = line[2] base_count_list = [] base_index = list(range(len(line[4]))) for i in base_index: if line[4][i] != '$': base_count_list.append(int(line[5][i])) if i + 1 == len(line[5]): break else: i += int(line[5][i + 1]) i += int(line[6][i]) i += int(line[7][i]) i += int(line[8][i]) i += int(line[9][i]) if ref_base == 'A': ref_base_index = base_index.index(0) ref_base_count = base_count_list.pop(ref_base_index) base_count_list.insert(0,-ref_base_count) elif ref_base == 'C': ref_base_index = base_index.index(1) ref_base_count = base_count_list.pop(ref_base_index) base_count_list.insert(1,-ref_base_count) elif ref_base == 'G': ref_base_index = base_index.index(2) ref_base_count = base_count_list.pop(ref_base_index) base_count_list.insert(2,-ref_base_count) elif ref_base == 'T': ref_base_index = base_index.index(3) ref_base_count = base_count_list.pop(ref_base_index) base_count_list.insert(3,-ref_base_count) yield {'chr':line[0],'pos':pos,'AC':base_count_list} def get_mutation_counts_from_mpileup(filename): """ Given mpileup file from samtools mpileup function, return mutation counts. Args: filename(str): path+filename of mpileup file Returns: A dictionary containing mutation counts, where keys are genomic positions. d = { 'chr': ['chrX', 'chrX', 'chrX', ...], 'pos': [10000L, ..., ...], 'A,C,G,T': [[5L ,7L ,9L ,11L], [13L ,15L ,17L ,19L], ...] } Mutation count order is A,C,G,T regardless of reference base at position. The first entry corresponds with reference allele count. This function assumes no insertions/deletions present at position. Special characters meaning something other than bases ('^','$', '.', '*') will not be counted as mutations but instead skipped over by this function. This function assumes input has already had '.' characters converted to corresponding uppercase bases using '-C' flag when running samtools mpileup This function assumes input has already had '*' characters converted to corresponding uppercase bases using '-o' flag when running samtools mpileup The output is NOT normalized by number reads covering position! This means that if there were more reads covering one position than another, the raw counts may appear higher even if actual mutation rate was lower! The raw counts should be normalized by number reads covering position before comparing mutation rates across positions! This can be done using e.g., normalize_mutation_counts_by_depth(d), below This can also be done using e.g., vcf_to_mutation_matrix(vcf_file), below If using vcf_to_mutation_matrix(), then normalization by read depth happens automatically! It is recommended you use vcf_to_mutation_matrix() rather than get_mutation_counts_from_mpileup() because vcf files contain much more useful information than mpileups! Mutational spectra calculated from outputs of these functions assume no insertions/deletions present at position! Special characters meaning something other than bases ('^','$', '.', '*') will not be counted as mutations but instead skipped over by this function! In practice this means only SNPs will be considered when calculating mutational spectra! This assumption may not hold true if sequencing technology used generates many indels or special characters! In such cases it would be best not to use this tool until such issues are addressed! Because this tool does not work well with indels or special characters! Mutations are assumed homopolymeric context-free unless otherwise specified! In other words context is assumed independent unless specified otherwise! Homopolymeric contexts are determined by counting identical bases immediately preceding variant site; e.g., AAAATTTTAAATTTTTGGG -> TTTT context; e.g., CCCCCCTTTGGGGCC -> C context; e.g., AGAGAGAGAGAG -> G context; Contexts are assumed independent unless otherwise specified! Contexts may overlap! E.g., AGAGCGCGCGCG -> GCG context; e.g., ATATATATATATAT -> TATA context; Contexts may span multiple chromosomes! E.g., chromosome AAAAAAAA...AAAAAA...AAAAAA...AAAAA...AAAACCCC... chromosome BBBBBBB...BBBBBB...BBBBBB...BBBBBB...CCCCCC... in this case CCCC contexts occur twice across two chromosomes! Contexts may span multiple scaffolds! E.g., scaffold AAAA..AAAA..AAAA..AAAA..AAAACCCC... scaffold BBBBB..BBBB..BBBB..BBBB..CCCCCC... in this case CCCC contexts occur twice across two scaffolds! Contexts may span entire chromosome/scaffold! E.g., chromosome/scaffold AAAA....AAAACCCC... in this case CCCC context occurs once spanning entire chromosome/scaffold! Contexts may occur multiple times within single chromosome/scaffold! E.g., chromosome/scaffold AAAAACCCCAAAACCCTTTCCCCAAACCCCAAACCCCAAACCCCAAACCCC... in this case CCCC occurs six times within single chromosome/scaffold! Contexts may occur multiple times within single chromosome/scaffold but only once spanning entire chromosome/scaffold! E.g., chromosome/scaffold AAAAACCCCAAAACCCTTTCCCCAAACCCCAAACCCCAAACCCCCCCCCCCCC... in this case CCCC occurs seven times within single chromosome/scaffold but only once spanning entire chromosome/scaffold! Mutational spectra calculated from outputs of these functions assume no insertions/deletions present at position! Special characters meaning something other than bases ('^','$', '.', '*') will not be counted as mutations but instead skipped over by this function! In practice this means only SNPs will be considered when calculating mutational spectra! This assumption may not hold true if sequencing technology used generates many indels or special characters! In such cases it would be best not to use this tool until such issues are addressed! Because this tool does not work well with indels or special characters! Mutational spectra calculated from outputs of these functions assume homopolymeric contexts unless otherwise specified! In other words contexts are assumed independent unless specified otherwise! Homopolymeric contexts are determined by counting identical bases immediately preceding variant site; e.g., AAAATTTTAAATTTTTGGG -> TTTT context; e.g., CCCCCCTTTGGGGCC -> C context; e.g., AGAGAGAGAGAG -> G context; Contexts may overlap! E.g., AGAGCGCGCGCG -> GCG context; e.g., ATATATATATATAT -> TATA context; Contexts may span multiple chromosomes! E.g., chromosome AAAAAAAA...AAAAAA...AAAAAA...AAAAA...AAAACCCC... chromosome BBBBBBB...BBBBBB...BBBBBB...BBBBBB...CCCCCC... in this case CCCC contexts occur twice across two chromosomes! Contexts may span multiple scaffolds! E.g., scaffold AAAA..AAAA..AAAA..AAAA..AAAACCCC... scaffold BBBBB..BBBB..BBBB..BBBB..CCCCCC... in this case CCCC contexts occur twice across two scaffolds! Contexts may span entire chromosome/scaffold! E.g., chromosome/scaffold AAAA....AAAACCCC... in this case CCCC context occurs once spanning entire chromosome/scaffold! Contexts may occur multiple times within single chromosome/scaffold! E.g., chromosome/scaffold AAAAACCCCAAAACCCTTTCCCCAAACCCCAAACCCCAAACCCCAAACCCC... in this case CCCC occurs six times within single chromosome/scaffold! Contexts may occur multiple times within single chromosome/scaffold but only once spanning entire chromosome/scaffold! E.g., chromosome/scaffold AAAAACCCCAAAACCCTTTCCCCAAACCCCAAACCCCAAACCCCCCCCCCCCC... in this case CCCC occurs seven times within single chromosome/scaffold but only once spanning entire chromosome/scaffold! Homopolymeric contexts mean that if you have e,g, GC->TA mutation then you need three separate matrices depending on whether it occurs after CCC..., TT..., AA..., etc... If you want non-homopolymeric contexts then you need four separate matrices depending on whether GC->TA mutation occurred after CG,...GC,...TC,...TG,... Homopolymeric non-overlapping contexts mean that if you have e,g, GC->TA mutation then you need six separate matrices depending on whether it occurred after CC..., CT..., CA..., GC..., GT..., GA... If you want overlapping homopolymeric non-overlapping contexts then you need nine separate matrices depending on whether GC->TA mutation occurred after CC..., CCT..., CCTT..., CT..., CTG,... CG,... CGG,... GA... If you want overlapping non-homopolymeric non-overlapping contexts then you need sixteen separate matrices depending on whether GC->TA mutation occurred after CG,... CGC,... CGGC,... GCC,... GCT,... GCA,... TGC,... TGCG,... TTGC,... TGA,... TGT,... CGC,... CGGC,... So basically there's lots of possibilities... Homopolymeric overlapping means counting identical bases immediately preceding variant site including those belonging same nucleotide repeat sequence extending beyond variant site; e,g, AATAATTATAATTATAATTATAATTATAATTATAATTACAATTACA -> A repeat sequence extends beyond variant site so TA->CA mutation occurring after fifth TA repeat is counted under same TA homopolymeric overlapping category even though sixth TA repeat extends beyond variant site Homopolymeric non-overlapping means counting identical bases immediately preceding variant site excluding those belonging same nucleotide repeat sequence extending beyond variant site; e,g, AATAATTATAATTATAATTATAATTATAATTATAATTACAATTACA -> A repeat sequence extends beyond variant site so TA->CA mutation occurring after fifth TA repeat is counted under same AA homopolymeric non-overlapping category because sixth TA repeat extends beyond variant site So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes, macro/microsatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, penta-nucleotide repeats, hexanucleotide repeats, septanucleotide repeats, octanucleotide repeats, nonalnucleotide repeats, decanucleotide repeats, and so forth... So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes, macro/microsatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, penta-nucleotide repeats, hexanucleotide repeats, septanucleotide repeats, octanucleotide repeats, nonalnucleotide repeats, decanucleotide repeats, and so forth... I'm sure there are more possibilities than those listed above but I think those cover most common cases anyway... So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes macro/microsatellites dinucleotide repeats trinucleotide repeats tetranucleotide repeats penta-nucleotide repeats hexanucleotide repeats septanucleotide repeats octanucleotide repeates nonalnucelotide repeates decanucelotide repeates and so forth... I'm sure there are more possibilities than those listed above but I think those cover most common cases anyway... So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes macro/microsatellites dinucelotide repeates trinucelotide repeates tetranucelotide repeates pentanucelotide repeates hexanucelotide repeates septanucelotide repeates octanucelotide repeates nonalnucelotide repeates decanucelotide repeates and so forth... I'm sure there are more possibilities than those listed above but I think those cover most common cases anyway... So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes macro/microsatellites dinucelotide repeate trinucelotide repeate tetranucelotide repeate pentanucelotide repeate hexanucelotide repeate septanucelotide repeate octanucelotide repeate nonalnucelotiderepeate decanucloteiderepeate and so forth... I'm sure there are more possibilities than those listed above but I think those cover most common cases anyway... So basically there's lots of possibilities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now... I'll just mention some common ones though: Common examples include: homo/heterozygous/haploid genomes macro/microsatellites dinucltoite repete trinuctloite repete tetranuctloite repete pentnucltoite repete hexnucltoite repete septnucltoite repete octnucltoite repete nonalucltoite repete decanucltoite repete and so forth.... I'm sure there are more possibilities than those listed above but I think they cover most common cases anyway.... So basically there's lots of possiblities... It would probably take me several hours just listing out all possible combinations here so I won't bother doing that now.... I'll jus mention some commen ones tho: Common exampels include: homo/heterozogous/haploid genoms, macromicrolisatites, dini/trini/tetra/penta/hepta/octa/nona/deca-nucltoites. and sff.